notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f90764c7-219e-43e0-a6d1-16667d7dd176",
   "metadata": {},
   "source": [
    "# Notebook setup and imports\n",
    "\n",
    "Uncomment and run the cell below to install the Python dependencies for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf4bc0a8-1770-40b1-b904-4331c5bd9d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pandas seaborn tqdm dissect.cobaltstrike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a73e778-c1d4-438b-a0e1-82e31e527687",
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import tqdm for progress bars :)\n",
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "# use seaborn for prettier graphs\n",
    "import seaborn\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# dissect.cobaltstrike imports\n",
    "from dissect.cobaltstrike.beacon import BeaconConfig, BeaconSetting\n",
    "from dissect.cobaltstrike import beacon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d9957a-89b9-445e-af36-94d6bd062a57",
   "metadata": {},
   "source": [
    "# Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56be9448-caf2-448a-b1c7-d5bff0d6cb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "gen_df = (pd.read_json(fname, lines=True) for fname in Path().glob(\"beacons-*.jsonl.gz\"))\n",
    "df = pd.concat(gen_df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc19c74-34b9-4ff7-aeba-c70e8e164b50",
   "metadata": {},
   "source": [
    "## Some dataset cleanup\n",
    "\n",
    "We cleanup some columns as type information is lost due to loading from JSON. We also add some extra useful columns for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd71b3d3-d88c-4bd6-8fe5-e4178eb7c40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do some cleanup\n",
    "df[\"collected_dt\"] = pd.to_datetime(df[\"collected_dt\"])\n",
    "df['year_month'] = df[\"collected_dt\"].map(lambda x: x.strftime(\"%Y-%m\"))\n",
    "df['year_month_dt'] = df[\"collected_dt\"].map(lambda x: x.date().replace(day=1))\n",
    "df['year_month_dt'] = df[\"collected_dt\"].map(lambda x: x.date().replace(day=1))\n",
    "df['collected_from_port'] = df['collected_from_port'].astype(pd.UInt16Dtype())\n",
    "df[\"SETTING_WATERMARK\"] = df[\"SETTING_WATERMARK\"].astype(pd.UInt32Dtype())\n",
    "\n",
    "# make digest.{md5,sha1,sha256} available as seperate columns\n",
    "df[\"md5\"] = df[\"digest\"].map(lambda x: x[\"md5\"])\n",
    "df[\"sha1\"] = df[\"digest\"].map(lambda x: x[\"sha1\"])\n",
    "df[\"sha256\"] = df[\"digest\"].map(lambda x: x[\"sha256\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b8975a4-9c12-4d70-9661-a8c0c44ed049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128340"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd256c76-0beb-4dde-84f1-e584aa3dd68e",
   "metadata": {},
   "source": [
    "## Some dataset statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd2ad7ce-6b0b-432d-b791-dc39a8ca1193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beacons</th>\n",
       "      <th>unique_beacons</th>\n",
       "      <th>uniq_teamservers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128340</td>\n",
       "      <td>34878</td>\n",
       "      <td>24913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beacons  unique_beacons  uniq_teamservers\n",
       "0   128340           34878             24913"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_beacon_count = len(df)\n",
    "uniq_beacon_count = df[\"md5\"].value_counts(dropna=False).count()\n",
    "uniq_teamserver_count = df[[\"collected_from_ip\", \"collected_from_port\", \"cert_der\"]].value_counts(dropna=False).count()\n",
    "\n",
    "pd.DataFrame.from_dict([{\n",
    "    \"beacons\": total_beacon_count,\n",
    "    \"unique_beacons\": uniq_beacon_count,\n",
    "    \"uniq_teamservers\": uniq_teamserver_count,\n",
    "}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "408d97a8-8edf-4f76-93f0-94d4e782c8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>port</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>443</td>\n",
       "      <td>80588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>42233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>3994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8080</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8888</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8443</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5555</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7777</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8088</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8081</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   port  counts\n",
       "0   443   80588\n",
       "1    80   42233\n",
       "2    53    3994\n",
       "3  8080     572\n",
       "4  8888     218\n",
       "5  8443     202\n",
       "6  5555     114\n",
       "7  7777      62\n",
       "8  8088      61\n",
       "9  8081      61"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 port count\n",
    "df[\"collected_from_port\"].value_counts().rename_axis('port').reset_index(name='counts').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4080d5f2-a45c-45f2-b85f-44e868a57a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "      <th>2019-03</th>\n",
       "      <th>2019-04</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-05</th>\n",
       "      <th>2021-06</th>\n",
       "      <th>2021-07</th>\n",
       "      <th>2021-08</th>\n",
       "      <th>2021-09</th>\n",
       "      <th>2021-10</th>\n",
       "      <th>2021-11</th>\n",
       "      <th>2021-12</th>\n",
       "      <th>2022-01</th>\n",
       "      <th>2022-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_from_port</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>254</td>\n",
       "      <td>272</td>\n",
       "      <td>218</td>\n",
       "      <td>300</td>\n",
       "      <td>257</td>\n",
       "      <td>262</td>\n",
       "      <td>203</td>\n",
       "      <td>166</td>\n",
       "      <td>145</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>944</td>\n",
       "      <td>982</td>\n",
       "      <td>680</td>\n",
       "      <td>715</td>\n",
       "      <td>888</td>\n",
       "      <td>896</td>\n",
       "      <td>728</td>\n",
       "      <td>537</td>\n",
       "      <td>523</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>377</td>\n",
       "      <td>484</td>\n",
       "      <td>356</td>\n",
       "      <td>533</td>\n",
       "      <td>534</td>\n",
       "      <td>586</td>\n",
       "      <td>450</td>\n",
       "      <td>301</td>\n",
       "      <td>287</td>\n",
       "      <td>171</td>\n",
       "      <td>...</td>\n",
       "      <td>1680</td>\n",
       "      <td>1724</td>\n",
       "      <td>1155</td>\n",
       "      <td>1147</td>\n",
       "      <td>1547</td>\n",
       "      <td>1490</td>\n",
       "      <td>998</td>\n",
       "      <td>825</td>\n",
       "      <td>478</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month           2018-07  2018-08  2018-09  2018-10  2018-11  2018-12  \\\n",
       "collected_from_port                                                         \n",
       "80                       254      272      218      300      257      262   \n",
       "443                      377      484      356      533      534      586   \n",
       "\n",
       "year_month           2019-01  2019-02  2019-03  2019-04  ...  2021-05  \\\n",
       "collected_from_port                                      ...            \n",
       "80                       203      166      145       88  ...      944   \n",
       "443                      450      301      287      171  ...     1680   \n",
       "\n",
       "year_month           2021-06  2021-07  2021-08  2021-09  2021-10  2021-11  \\\n",
       "collected_from_port                                                         \n",
       "80                       982      680      715      888      896      728   \n",
       "443                     1724     1155     1147     1547     1490      998   \n",
       "\n",
       "year_month           2021-12  2022-01  2022-02  \n",
       "collected_from_port                             \n",
       "80                       537      523      526  \n",
       "443                      825      478      774  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PORTS = [443, 80]\n",
    "\n",
    "df_teamservers = df.drop_duplicates(['collected_from_ip','collected_from_port', 'cert_der', 'year_month'], keep='last')\n",
    "g = df_teamservers[df_teamservers[\"collected_from_port\"].isin(PORTS)].groupby([\"year_month\", \"collected_from_port\"])\n",
    "# g.size()\n",
    "g.size().unstack(\"year_month\")\n",
    "# g.size().unstack()\n",
    "# print(g.size().unstack(\"year_month\").to_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4be7e3da-a990-4b6f-8142-2b34f97a84be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_month'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.size().unstack().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b152de33-8fcc-45cb-81ab-fbf25b0cbf84",
   "metadata": {},
   "source": [
    "# Beacon metadata research\n",
    "\n",
    "We first instantiate a `BeaconConfig` object from the `config_block` data and add this as new column called `bconfig`.\n",
    "This allows us to extract additional Beacon information easier.\n",
    "\n",
    "See also the documentation of `dissect.cobaltstrike`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8900053-22a6-4f6c-ba08-133e6a91b451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64bc353d84aa4e6eaf8e17d9edd29624",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def beacon_config(row):\n",
    "    bconfig = BeaconConfig(base64.b64decode(row.config_block))\n",
    "    bconfig.xorkey = int(row.xorkey, 0)\n",
    "    bconfig.xorencoded = row.xorencoded\n",
    "    bconfig.pe_export_stamp = row.pe_export_stamp\n",
    "    bconfig.pe_compile_stamp = row.pe_compile_stamp\n",
    "    return bconfig\n",
    "\n",
    "df[\"bconfig\"] = df.progress_apply(beacon_config, axis=1)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cb9da9-1626-420e-a666-1c3fc7e7fc57",
   "metadata": {},
   "source": [
    "## Beacon version overviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a01c353-e418-49d2-a281-2b5ddd148fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d00f6f804dc44f269710239140160caa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0         Cobalt Strike 3.x\n",
       "1         Cobalt Strike 3.x\n",
       "2         Cobalt Strike 3.x\n",
       "3         Cobalt Strike 3.x\n",
       "4         Cobalt Strike 3.x\n",
       "                ...        \n",
       "128335    Cobalt Strike 4.5\n",
       "128336    Cobalt Strike 4.4\n",
       "128337    Cobalt Strike 4.5\n",
       "128338    Cobalt Strike 4.3\n",
       "128339    Cobalt Strike 4.0\n",
       "Name: agg_version, Length: 128340, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def aggregate_old_version(version):\n",
    "    \"\"\"This aggregates everything older than 4.0 as 3.x\"\"\"\n",
    "    ver = beacon.BeaconVersion(version)\n",
    "    if ver.tuple is None:\n",
    "        print(version)\n",
    "    if ver.tuple < (4, 0):\n",
    "        return \"Cobalt Strike 3.x\"\n",
    "    return ver.version_string\n",
    "df[\"agg_version\"] = df[\"beacon_version\"].progress_apply(aggregate_old_version)\n",
    "df[\"agg_version\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7bee1317-5796-4ca1-abd1-2cc4b2caf799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_month'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this makes sure that a unique beacon is only counted once per year_month\n",
    "df_year_month = df.drop_duplicates(['md5','collected_from_ip','collected_from_port','year_month'], keep='last')\n",
    "\n",
    "versions = df_year_month.groupby([\"year_month\", \"agg_version\"]).size().unstack().fillna(0)\n",
    "versions.plot.bar(stacked=True, figsize=(20, 10))\n",
    "# print(versions.to_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e0b2cad-f7e1-458d-a329-82297d262b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "870d3b6ff52e44888f4f33a216b98aeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_month'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def aggregate_old_version2(version):\n",
    "    version = beacon.BeaconVersion(version)\n",
    "    if version.tuple <= (3, 12):\n",
    "        return \"Cobalt Strike <= 3.12\"\n",
    "    return version.version_string\n",
    "df[\"agg_version2\"] = df[\"beacon_version\"].progress_apply(aggregate_old_version2)\n",
    "\n",
    "# this makes sure that a unique beacon is only counted once per year_month\n",
    "df_year_month = df.drop_duplicates(['md5','year_month'], keep='last')\n",
    "\n",
    "versions = df_year_month.groupby([\"year_month\", \"agg_version2\"]).size().unstack().fillna(0)\n",
    "versions.plot.bar(stacked=True, colormap='tab10', figsize=(20, 10))\n",
    "# print(versions.to_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b5ed9846-fa9a-4110-b2cb-8f62f8463da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>agg_version2</th>\n",
       "      <th>Cobalt Strike 3.13</th>\n",
       "      <th>Cobalt Strike 3.14</th>\n",
       "      <th>Cobalt Strike 4.0</th>\n",
       "      <th>Cobalt Strike 4.1</th>\n",
       "      <th>Cobalt Strike 4.2</th>\n",
       "      <th>Cobalt Strike 4.3</th>\n",
       "      <th>Cobalt Strike 4.4</th>\n",
       "      <th>Cobalt Strike 4.5</th>\n",
       "      <th>Cobalt Strike &lt;= 3.12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>22</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>68</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>95</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>48</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10</th>\n",
       "      <td>135</td>\n",
       "      <td>448</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11</th>\n",
       "      <td>135</td>\n",
       "      <td>547</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12</th>\n",
       "      <td>146</td>\n",
       "      <td>599</td>\n",
       "      <td>31</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01</th>\n",
       "      <td>120</td>\n",
       "      <td>501</td>\n",
       "      <td>128</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02</th>\n",
       "      <td>110</td>\n",
       "      <td>409</td>\n",
       "      <td>256</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03</th>\n",
       "      <td>133</td>\n",
       "      <td>499</td>\n",
       "      <td>480</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04</th>\n",
       "      <td>110</td>\n",
       "      <td>391</td>\n",
       "      <td>599</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05</th>\n",
       "      <td>161</td>\n",
       "      <td>506</td>\n",
       "      <td>1077</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06</th>\n",
       "      <td>107</td>\n",
       "      <td>380</td>\n",
       "      <td>1051</td>\n",
       "      <td>8</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07</th>\n",
       "      <td>122</td>\n",
       "      <td>400</td>\n",
       "      <td>1186</td>\n",
       "      <td>181</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08</th>\n",
       "      <td>60</td>\n",
       "      <td>235</td>\n",
       "      <td>689</td>\n",
       "      <td>148</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>97</td>\n",
       "      <td>302</td>\n",
       "      <td>1154</td>\n",
       "      <td>968</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>85</td>\n",
       "      <td>291</td>\n",
       "      <td>1291</td>\n",
       "      <td>1052</td>\n",
       "      <td>111</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>58</td>\n",
       "      <td>203</td>\n",
       "      <td>781</td>\n",
       "      <td>644</td>\n",
       "      <td>273</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>42</td>\n",
       "      <td>142</td>\n",
       "      <td>577</td>\n",
       "      <td>440</td>\n",
       "      <td>388</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>45</td>\n",
       "      <td>167</td>\n",
       "      <td>706</td>\n",
       "      <td>524</td>\n",
       "      <td>916</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>50</td>\n",
       "      <td>126</td>\n",
       "      <td>650</td>\n",
       "      <td>467</td>\n",
       "      <td>854</td>\n",
       "      <td>85</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04</th>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>851</td>\n",
       "      <td>611</td>\n",
       "      <td>977</td>\n",
       "      <td>415</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>35</td>\n",
       "      <td>86</td>\n",
       "      <td>719</td>\n",
       "      <td>534</td>\n",
       "      <td>837</td>\n",
       "      <td>649</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>33</td>\n",
       "      <td>91</td>\n",
       "      <td>625</td>\n",
       "      <td>549</td>\n",
       "      <td>780</td>\n",
       "      <td>936</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>364</td>\n",
       "      <td>300</td>\n",
       "      <td>404</td>\n",
       "      <td>678</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "      <td>353</td>\n",
       "      <td>295</td>\n",
       "      <td>369</td>\n",
       "      <td>708</td>\n",
       "      <td>71</td>\n",
       "      <td>-</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>458</td>\n",
       "      <td>356</td>\n",
       "      <td>498</td>\n",
       "      <td>937</td>\n",
       "      <td>271</td>\n",
       "      <td>-</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>408</td>\n",
       "      <td>353</td>\n",
       "      <td>428</td>\n",
       "      <td>895</td>\n",
       "      <td>407</td>\n",
       "      <td>-</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>284</td>\n",
       "      <td>276</td>\n",
       "      <td>235</td>\n",
       "      <td>548</td>\n",
       "      <td>535</td>\n",
       "      <td>-</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>224</td>\n",
       "      <td>212</td>\n",
       "      <td>166</td>\n",
       "      <td>391</td>\n",
       "      <td>473</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>168</td>\n",
       "      <td>159</td>\n",
       "      <td>139</td>\n",
       "      <td>265</td>\n",
       "      <td>337</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>194</td>\n",
       "      <td>159</td>\n",
       "      <td>162</td>\n",
       "      <td>394</td>\n",
       "      <td>437</td>\n",
       "      <td>145</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "agg_version2 Cobalt Strike 3.13 Cobalt Strike 3.14 Cobalt Strike 4.0  \\\n",
       "year_month                                                             \n",
       "2018-07                       -                  -                 -   \n",
       "2018-08                       -                  -                 -   \n",
       "2018-09                       -                  -                 -   \n",
       "2018-10                       -                  -                 -   \n",
       "2018-11                       -                  -                 -   \n",
       "2018-12                       -                  -                 -   \n",
       "2019-01                      22                  -                 -   \n",
       "2019-02                      68                  -                 -   \n",
       "2019-03                      95                  -                 -   \n",
       "2019-04                      48                  -                 -   \n",
       "2019-10                     135                448                 1   \n",
       "2019-11                     135                547                 -   \n",
       "2019-12                     146                599                31   \n",
       "2020-01                     120                501               128   \n",
       "2020-02                     110                409               256   \n",
       "2020-03                     133                499               480   \n",
       "2020-04                     110                391               599   \n",
       "2020-05                     161                506              1077   \n",
       "2020-06                     107                380              1051   \n",
       "2020-07                     122                400              1186   \n",
       "2020-08                      60                235               689   \n",
       "2020-10                      97                302              1154   \n",
       "2020-11                      85                291              1291   \n",
       "2020-12                      58                203               781   \n",
       "2021-01                      42                142               577   \n",
       "2021-02                      45                167               706   \n",
       "2021-03                      50                126               650   \n",
       "2021-04                      39                110               851   \n",
       "2021-05                      35                 86               719   \n",
       "2021-06                      33                 91               625   \n",
       "2021-07                      16                 43               364   \n",
       "2021-08                      20                 47               353   \n",
       "2021-09                      25                 55               458   \n",
       "2021-10                      20                 50               408   \n",
       "2021-11                      23                 34               284   \n",
       "2021-12                      17                 20               224   \n",
       "2022-01                      19                  9               168   \n",
       "2022-02                      21                 11               194   \n",
       "\n",
       "agg_version2 Cobalt Strike 4.1 Cobalt Strike 4.2 Cobalt Strike 4.3  \\\n",
       "year_month                                                           \n",
       "2018-07                      -                 -                 -   \n",
       "2018-08                      -                 -                 -   \n",
       "2018-09                      -                 -                 -   \n",
       "2018-10                      -                 -                 -   \n",
       "2018-11                      -                 -                 -   \n",
       "2018-12                      -                 -                 -   \n",
       "2019-01                      -                 -                 -   \n",
       "2019-02                      -                 -                 -   \n",
       "2019-03                      -                 -                 -   \n",
       "2019-04                      -                 -                 -   \n",
       "2019-10                      -                 -                 -   \n",
       "2019-11                      -                 -                 -   \n",
       "2019-12                      -                 -                 -   \n",
       "2020-01                      -                 -                 -   \n",
       "2020-02                      -                 -                 -   \n",
       "2020-03                      -                 -                 -   \n",
       "2020-04                      -                 -                 -   \n",
       "2020-05                      -                 -                 -   \n",
       "2020-06                      8                 -                 -   \n",
       "2020-07                    181                 -                 -   \n",
       "2020-08                    148                 -                 -   \n",
       "2020-10                    968                 1                 -   \n",
       "2020-11                   1052               111                 -   \n",
       "2020-12                    644               273                 -   \n",
       "2021-01                    440               388                 -   \n",
       "2021-02                    524               916                 -   \n",
       "2021-03                    467               854                85   \n",
       "2021-04                    611               977               415   \n",
       "2021-05                    534               837               649   \n",
       "2021-06                    549               780               936   \n",
       "2021-07                    300               404               678   \n",
       "2021-08                    295               369               708   \n",
       "2021-09                    356               498               937   \n",
       "2021-10                    353               428               895   \n",
       "2021-11                    276               235               548   \n",
       "2021-12                    212               166               391   \n",
       "2022-01                    159               139               265   \n",
       "2022-02                    159               162               394   \n",
       "\n",
       "agg_version2 Cobalt Strike 4.4 Cobalt Strike 4.5 Cobalt Strike <= 3.12  \n",
       "year_month                                                              \n",
       "2018-07                      -                 -                   633  \n",
       "2018-08                      -                 -                   830  \n",
       "2018-09                      -                 -                   549  \n",
       "2018-10                      -                 -                   933  \n",
       "2018-11                      -                 -                   864  \n",
       "2018-12                      -                 -                   995  \n",
       "2019-01                      -                 -                   666  \n",
       "2019-02                      -                 -                   398  \n",
       "2019-03                      -                 -                   363  \n",
       "2019-04                      -                 -                   214  \n",
       "2019-10                      -                 -                    74  \n",
       "2019-11                      -                 -                    85  \n",
       "2019-12                      -                 -                    93  \n",
       "2020-01                      -                 -                   109  \n",
       "2020-02                      -                 -                    58  \n",
       "2020-03                      -                 -                   101  \n",
       "2020-04                      -                 -                   128  \n",
       "2020-05                      -                 -                   181  \n",
       "2020-06                      -                 -                   138  \n",
       "2020-07                      -                 -                   115  \n",
       "2020-08                      -                 -                    76  \n",
       "2020-10                      -                 -                    68  \n",
       "2020-11                      -                 -                    56  \n",
       "2020-12                      -                 -                    41  \n",
       "2021-01                      -                 -                    38  \n",
       "2021-02                      -                 -                    42  \n",
       "2021-03                      -                 -                    33  \n",
       "2021-04                      -                 -                    39  \n",
       "2021-05                      -                 -                    27  \n",
       "2021-06                      -                 -                    26  \n",
       "2021-07                      -                 -                    16  \n",
       "2021-08                     71                 -                    12  \n",
       "2021-09                    271                 -                    15  \n",
       "2021-10                    407                 -                    13  \n",
       "2021-11                    535                 -                    11  \n",
       "2021-12                    473                16                     4  \n",
       "2022-01                    337                39                     6  \n",
       "2022-02                    437               145                     4  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_year_month.groupby([\"year_month\", \"agg_version2\"]).size().unstack(fill_value=\"-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2bcf34-2f02-4530-a071-37c970704c0e",
   "metadata": {},
   "source": [
    "## Watermark Usage\n",
    "Watermark, also known as customer or authorization id, should be unique. If they are re-used or have an \"off\" value, it can indicate a pirated version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f3dff2f-1d6a-484b-8698-c1b3291f3fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SETTING_WATERMARK</th>\n",
       "      <th>SETTING_WATERMARK_HEX</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>305419896</td>\n",
       "      <td>0x12345678</td>\n",
       "      <td>7931</td>\n",
       "      <td>23.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0x0</td>\n",
       "      <td>7727</td>\n",
       "      <td>22.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359593325</td>\n",
       "      <td>0x5109bf6d</td>\n",
       "      <td>2362</td>\n",
       "      <td>6.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1873433027</td>\n",
       "      <td>0x6faa51c3</td>\n",
       "      <td>1492</td>\n",
       "      <td>4.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>426352781</td>\n",
       "      <td>0x1969a08d</td>\n",
       "      <td>1448</td>\n",
       "      <td>4.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1234567890</td>\n",
       "      <td>0x499602d2</td>\n",
       "      <td>700</td>\n",
       "      <td>2.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1580103814</td>\n",
       "      <td>0x5e2e7886</td>\n",
       "      <td>694</td>\n",
       "      <td>2.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0x1</td>\n",
       "      <td>689</td>\n",
       "      <td>2.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1580103824</td>\n",
       "      <td>0x5e2e7890</td>\n",
       "      <td>674</td>\n",
       "      <td>2.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16777216</td>\n",
       "      <td>0x1000000</td>\n",
       "      <td>339</td>\n",
       "      <td>1.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>0x3</td>\n",
       "      <td>331</td>\n",
       "      <td>0.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1711276032</td>\n",
       "      <td>0x66000000</td>\n",
       "      <td>250</td>\n",
       "      <td>0.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>305419776</td>\n",
       "      <td>0x12345600</td>\n",
       "      <td>196</td>\n",
       "      <td>0.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>0x6</td>\n",
       "      <td>123</td>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>0x8</td>\n",
       "      <td>104</td>\n",
       "      <td>0.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>388888888</td>\n",
       "      <td>0x172df938</td>\n",
       "      <td>94</td>\n",
       "      <td>0.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>352733121</td>\n",
       "      <td>0x150647c1</td>\n",
       "      <td>92</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>555758901</td>\n",
       "      <td>0x21203535</td>\n",
       "      <td>63</td>\n",
       "      <td>0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8848</td>\n",
       "      <td>0x2290</td>\n",
       "      <td>54</td>\n",
       "      <td>0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>452436291</td>\n",
       "      <td>0x1af7a143</td>\n",
       "      <td>45</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SETTING_WATERMARK SETTING_WATERMARK_HEX  count percent\n",
       "0           305419896            0x12345678   7931  23.49%\n",
       "1                   0                   0x0   7727  22.88%\n",
       "2          1359593325            0x5109bf6d   2362   6.99%\n",
       "3          1873433027            0x6faa51c3   1492   4.42%\n",
       "4           426352781            0x1969a08d   1448   4.29%\n",
       "5          1234567890            0x499602d2    700   2.07%\n",
       "6          1580103814            0x5e2e7886    694   2.06%\n",
       "7                   1                   0x1    689   2.04%\n",
       "8          1580103824            0x5e2e7890    674    2.0%\n",
       "9            16777216             0x1000000    339    1.0%\n",
       "10                  3                   0x3    331   0.98%\n",
       "11         1711276032            0x66000000    250   0.74%\n",
       "12          305419776            0x12345600    196   0.58%\n",
       "13                  6                   0x6    123   0.36%\n",
       "14                  8                   0x8    104   0.31%\n",
       "15          388888888            0x172df938     94   0.28%\n",
       "16          352733121            0x150647c1     92   0.27%\n",
       "17          555758901            0x21203535     63   0.19%\n",
       "18               8848                0x2290     54   0.16%\n",
       "19          452436291            0x1af7a143     45   0.13%"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uniq_beacons = df.drop_duplicates(['md5'], keep='last')\n",
    "# df_uniq_beacons.SETTING_WATERMARK = df_uniq_beacons.SETTING_WATERMARK.astype(pd.UInt32Dtype())\n",
    "watermarks = df_uniq_beacons[[\"SETTING_WATERMARK\", \"SETTING_WATERMARK_HEX\"]].value_counts().rename(\"count\").reset_index()\n",
    "watermarks[\"percent\"] = (watermarks[\"count\"] / watermarks[\"count\"].sum() * 100).round(2).astype(str) + \"%\"\n",
    "watermarks.head(20)\n",
    "# print(watermarks.head(50).to_csv())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43d7d65-6847-4bac-a385-a055aaf99414",
   "metadata": {},
   "source": [
    "## Public Key Usage\n",
    "Public keys should be unique, as the private key is normally generated upon the first start of the Team Server. If they are re-used it can indicate a pirated version that is packaged with the public and private key pair. The public key can also be used for pivotting and correlation in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "352d52ef-3656-497f-888d-666423f63c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SETTING_PUBKEY_SHA256</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36aff0b273cb7aa704e4219ad3be78defcc8c1d7ecb779...</td>\n",
       "      <td>3015</td>\n",
       "      <td>8.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c6df9424b64199e5a30097bb1324c89197e831703a8c7f...</td>\n",
       "      <td>1735</td>\n",
       "      <td>4.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c05695c59a64888a6c603a014ef89ed8f51bbdf3af7432...</td>\n",
       "      <td>509</td>\n",
       "      <td>1.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0d2c1591c8250291b1242214213004865099f401e20e7b...</td>\n",
       "      <td>401</td>\n",
       "      <td>1.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a593dd99a9d4845ce34857a365f79c2621d3738d55a21f...</td>\n",
       "      <td>347</td>\n",
       "      <td>0.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71fab2149cbdce552f00e6d75372494d3f7755d366fd68...</td>\n",
       "      <td>109</td>\n",
       "      <td>0.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>afb21ece0b47440acc3ee710800b7741fd7d6b3b88efc1...</td>\n",
       "      <td>94</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0a66c65671962a56cffe273bbddad35e1f7c65cc97e62c...</td>\n",
       "      <td>91</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0ea137caee35d5f96a7b70647f63b58cd1a71829f46fd4...</td>\n",
       "      <td>85</td>\n",
       "      <td>0.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>969a1320d8b2533247921024352fa0b684d6f326f9ad67...</td>\n",
       "      <td>77</td>\n",
       "      <td>0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4568c7bda707e70bf74de1ab8aa1c0bc0b03eb81b1b984...</td>\n",
       "      <td>68</td>\n",
       "      <td>0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31cc95de7c904dcacde6d3777d259deb267fe97a9f46da...</td>\n",
       "      <td>59</td>\n",
       "      <td>0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b7cccdd896c9484c1aa012e70c7ce3a2d02277acea0899...</td>\n",
       "      <td>58</td>\n",
       "      <td>0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49661c47b563e98f50bca31310bfe18a411efff8200b02...</td>\n",
       "      <td>53</td>\n",
       "      <td>0.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4c714d896ccc2bb1cf3049466a7f570d37a9397d1b66f4...</td>\n",
       "      <td>52</td>\n",
       "      <td>0.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5ba2805db6d7f71a8b54076dfdeca9c5f0cd0ca5e84f57...</td>\n",
       "      <td>51</td>\n",
       "      <td>0.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3b03fb7ef4ec7c2430d355b4f0a313b01112df5854b5e0...</td>\n",
       "      <td>49</td>\n",
       "      <td>0.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>a4c0f7a24baeb5500cbbd25f829007787de5e699b9c577...</td>\n",
       "      <td>47</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6b9e90ff2cbfaf159ffc96915c868d5c89e410738f0fd6...</td>\n",
       "      <td>45</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ce1ca4db66e5769b18702217f4de881a18f79117fae422...</td>\n",
       "      <td>44</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                SETTING_PUBKEY_SHA256  count percent\n",
       "0   36aff0b273cb7aa704e4219ad3be78defcc8c1d7ecb779...   3015   8.64%\n",
       "1   c6df9424b64199e5a30097bb1324c89197e831703a8c7f...   1735   4.97%\n",
       "2   c05695c59a64888a6c603a014ef89ed8f51bbdf3af7432...    509   1.46%\n",
       "3   0d2c1591c8250291b1242214213004865099f401e20e7b...    401   1.15%\n",
       "4   a593dd99a9d4845ce34857a365f79c2621d3738d55a21f...    347   0.99%\n",
       "5   71fab2149cbdce552f00e6d75372494d3f7755d366fd68...    109   0.31%\n",
       "6   afb21ece0b47440acc3ee710800b7741fd7d6b3b88efc1...     94   0.27%\n",
       "7   0a66c65671962a56cffe273bbddad35e1f7c65cc97e62c...     91   0.26%\n",
       "8   0ea137caee35d5f96a7b70647f63b58cd1a71829f46fd4...     85   0.24%\n",
       "9   969a1320d8b2533247921024352fa0b684d6f326f9ad67...     77   0.22%\n",
       "10  4568c7bda707e70bf74de1ab8aa1c0bc0b03eb81b1b984...     68   0.19%\n",
       "11  31cc95de7c904dcacde6d3777d259deb267fe97a9f46da...     59   0.17%\n",
       "12  b7cccdd896c9484c1aa012e70c7ce3a2d02277acea0899...     58   0.17%\n",
       "13  49661c47b563e98f50bca31310bfe18a411efff8200b02...     53   0.15%\n",
       "14  4c714d896ccc2bb1cf3049466a7f570d37a9397d1b66f4...     52   0.15%\n",
       "15  5ba2805db6d7f71a8b54076dfdeca9c5f0cd0ca5e84f57...     51   0.15%\n",
       "16  3b03fb7ef4ec7c2430d355b4f0a313b01112df5854b5e0...     49   0.14%\n",
       "17  a4c0f7a24baeb5500cbbd25f829007787de5e699b9c577...     47   0.13%\n",
       "18  6b9e90ff2cbfaf159ffc96915c868d5c89e410738f0fd6...     45   0.13%\n",
       "19  ce1ca4db66e5769b18702217f4de881a18f79117fae422...     44   0.13%"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uniq_beacons = df.drop_duplicates(['md5'], keep='last')\n",
    "pubkey = df_uniq_beacons[[\"SETTING_PUBKEY_SHA256\"]].value_counts().rename(\"count\").reset_index()\n",
    "pubkey[\"percent\"] = (pubkey[\"count\"] / pubkey[\"count\"].sum() * 100).round(2).astype(str) + \"%\"\n",
    "pubkey.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2183567-0e9a-498f-bd97-ebdfad1b1243",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Top XOR keys\n",
    "\n",
    "The default XOR keys are `0x69` and `0x2e`. Other XOR keys inidicate modified Team Servers and beacons as this is not customizable by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31cac401-c99c-489d-ad75-9814bcd31001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seen</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0x2e</th>\n",
       "      <td>26054</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x69</th>\n",
       "      <td>8631</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x3e</th>\n",
       "      <td>52</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x5a</th>\n",
       "      <td>45</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x3a</th>\n",
       "      <td>26</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0xfc</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x2f</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x4d</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x8b</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x90</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x5f</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x5e</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x3d</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0xa9</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x5d</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x9f</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x9e</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x64</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x86</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x8f</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0xcc</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0xee</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x38</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x6c</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x44</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x4e</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0x6d</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seen  percent\n",
       "0x2e  26054     74.7\n",
       "0x69   8631     24.7\n",
       "0x3e     52      0.1\n",
       "0x5a     45      0.1\n",
       "0x3a     26      0.1\n",
       "0xfc     11      0.0\n",
       "0x2f      7      0.0\n",
       "0x4d      7      0.0\n",
       "0x8b      6      0.0\n",
       "0x90      6      0.0\n",
       "0x5f      5      0.0\n",
       "0x5e      4      0.0\n",
       "0x3d      4      0.0\n",
       "0xa9      3      0.0\n",
       "0x5d      2      0.0\n",
       "0x9f      2      0.0\n",
       "0x9e      2      0.0\n",
       "0x64      2      0.0\n",
       "0x86      1      0.0\n",
       "0x8f      1      0.0\n",
       "0xcc      1      0.0\n",
       "0xee      1      0.0\n",
       "0x38      1      0.0\n",
       "0x6c      1      0.0\n",
       "0x44      1      0.0\n",
       "0x4e      1      0.0\n",
       "0x6d      1      0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uniq_beacons = df.drop_duplicates(['md5'], keep='last')\n",
    "c = df_uniq_beacons[\"xorkey\"].value_counts()\n",
    "p = df_uniq_beacons[\"xorkey\"].value_counts(normalize=True).mul(100).round(1)\n",
    "\n",
    "cp = pd.concat([c, p], axis=1, keys=[\"seen\", \"percent\"])\n",
    "cp\n",
    "# print(cp.to_csv())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98ec8f6-d0df-4d22-be91-936f79bddbf7",
   "metadata": {},
   "source": [
    "# Finding Malicious Beacons\n",
    "\n",
    "Determine if a beacon payload is malicious using some indicators. It's by no means complete or fool proof; improvements are most welcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b036e81f-d028-43e6-a01b-4bf6e2e736d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac57b3f1e100482cb3b56eeddc895319",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reused watermarks, we choose the top 20 of the most used watermarks\n",
    "REUSED_WATERMARKS = list(watermarks[\"SETTING_WATERMARK\"].head(20))\n",
    "\n",
    "# Reused public keys, we choose the top 5 of the most reused pubkeys\n",
    "REUSED_PUBKEYS = list(pubkey[\"SETTING_PUBKEY_SHA256\"].head(5))\n",
    "\n",
    "# Known good cobaltstrike.jar MD5's\n",
    "KNOWN = [\n",
    "    \"303ae5ba3c016e498624505880fad314\", # Cobalt Strike 4.0 (Dec 05, 2019) \n",
    "    \"75612d550e900d0ae96c147dc931556f\", # Cobalt Strike 4.0 (Feb 22, 2020) [bugfixes]\n",
    "    \"367635691cdd70722ef5706a8f0ca7e6\", # Cobalt Strike 4.1 (Jun 25, 2020)\n",
    "    \"b54afe01ec6a75edf35e1a44f8bd3929\", # Cobalt Strike 4.2 (Nov 06, 2020)\n",
    "    \"32cd41edf0810c5b5f498edf4731cc6d\", # Cobalt Strike 4.3 (Mar 11, 2021)\n",
    "    \"b2736f1cbba90d42286fc42bfba74f4d\", # Cobalt Strike 4.3 (Mar 17, 2021) [bugfixes]\n",
    "    \"222b8f27dbdfba8ddd559eeca27ea648\", # Cobalt Strike 4.4 (Aug 04, 2021)\n",
    "    \"04e0a11be59147a8d73d2b3e9fea832c\", # Cobalt Strike 4.5 (Dec 14, 2021)\n",
    "]\n",
    "\n",
    "def beacon_modifications(row):\n",
    "    bconfig = row.bconfig\n",
    "\n",
    "    # md5(cobaltstrike.jar)\n",
    "    data = bconfig.raw_settings.get(BeaconSetting.SETTING_PROCINJ_STUB, \"\")\n",
    "    jar_md5 = None\n",
    "    if len(data) == 16:\n",
    "        jar_md5 = data.hex()\n",
    "    \n",
    "    mods = []\n",
    "    if bconfig.xorkey not in (0x2e, 0x69):\n",
    "        # non default XOR keys indicate modification\n",
    "        mods.append(\"modified_xorkey\")\n",
    "    if bconfig.xorencoded and bconfig.is_trial:        \n",
    "        # xorencoded payloads are only available in the licensed versions\n",
    "        mods.append(\"modified_trial\")\n",
    "    if bconfig.watermark is not None and bconfig.watermark < 1000:\n",
    "        # low watermark number\n",
    "        mods.append(\"bad_watermark\")\n",
    "    if bconfig.watermark in REUSED_WATERMARKS:\n",
    "        # watermark in is in the top reused watermarks\n",
    "        mods.append(\"reused_watermark\")\n",
    "    if row.SETTING_PUBKEY_SHA256 in REUSED_PUBKEYS:\n",
    "        # public key is in the top reused pubkeys\n",
    "        mods.append(\"reused_pubkey\")\n",
    "    if row.SETTING_WATERMARKHASH == \"bnVsbA==\":\n",
    "        # looks like 4.5 is already leaked/pirated, the watermarkhash is set to b\"null\"\n",
    "        mods.append(\"bad_watermarkhash\")\n",
    "    if jar_md5 and bconfig.version.tuple >= (3, 14) and jar_md5 not in KNOWN:\n",
    "        # the MD5 of cobaltstrike.jar is not \"official\"\n",
    "        mods.append(\"modified_jar\")\n",
    "    if mods:\n",
    "        return \"+\".join(mods)\n",
    "    return \"normal\"\n",
    "\n",
    "df[\"modifications\"] = df.progress_apply(beacon_modifications, axis=1)\n",
    "df[\"is_modified\"] = df[\"modifications\"] != \"normal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17dc7b03-c9ed-4c78-9350-bb48d72bfca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>modifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reused_watermark</td>\n",
       "      <td>40589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>40167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bad_watermark+reused_watermark</td>\n",
       "      <td>24913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>reused_watermark+reused_pubkey</td>\n",
       "      <td>11775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bad_watermark+reused_watermark+reused_pubkey</td>\n",
       "      <td>5911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>modified_trial</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>modified_trial+bad_watermark+reused_watermark</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>modified_xorkey+reused_watermark</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>modified_trial+reused_watermark</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>modified_xorkey+bad_watermark+reused_watermark</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>reused_pubkey</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bad_watermark</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>modified_xorkey+bad_watermark+reused_watermark...</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>modified_xorkey+reused_watermark+reused_pubkey</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>modified_xorkey</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>bad_watermark+reused_watermark+bad_watermarkhash</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>modified_trial+bad_watermark</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                index  modifications\n",
       "0                                    reused_watermark          40589\n",
       "1                                              normal          40167\n",
       "2                      bad_watermark+reused_watermark          24913\n",
       "3                      reused_watermark+reused_pubkey          11775\n",
       "4        bad_watermark+reused_watermark+reused_pubkey           5911\n",
       "5                                      modified_trial           2390\n",
       "6       modified_trial+bad_watermark+reused_watermark           1585\n",
       "7                    modified_xorkey+reused_watermark            289\n",
       "8                     modified_trial+reused_watermark            258\n",
       "9      modified_xorkey+bad_watermark+reused_watermark            160\n",
       "10                                      reused_pubkey            139\n",
       "11                                      bad_watermark             55\n",
       "12  modified_xorkey+bad_watermark+reused_watermark...             43\n",
       "13     modified_xorkey+reused_watermark+reused_pubkey             31\n",
       "14                                    modified_xorkey             13\n",
       "15   bad_watermark+reused_watermark+bad_watermarkhash             13\n",
       "16                       modified_trial+bad_watermark              9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"modifications\"].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "59a50ef1-6e7d-4716-b82f-bd4d3519d705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'modified vs normal'}, xlabel='year_month'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd = df.drop_duplicates(['md5', 'year_month'], keep='last')\n",
    "mods = dd.groupby([\"year_month\", \"modifications\"]).size().unstack().fillna(0)\n",
    "is_mods = dd.groupby([\"year_month\", \"is_modified\"]).size().unstack().fillna(0)\n",
    "\n",
    "fig, axs = plt.subplots(2, )\n",
    "mods.plot.bar(stacked=True, colormap='tab20', ax=axs[0], figsize=(20, 20), title=\"modified beacons\")\n",
    "is_mods.plot.bar(colormap='tab20', ax=axs[1], figsize=(20, 20), title=\"modified vs normal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1856c16-c9aa-4838-9422-e0cc34244509",
   "metadata": {},
   "source": [
    "## Malicious beacons per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd58e3e3-64ef-4706-a5b4-b66f88f606e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_modified</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>total</th>\n",
       "      <th>modified_p</th>\n",
       "      <th>normal_p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07</th>\n",
       "      <td>489</td>\n",
       "      <td>144</td>\n",
       "      <td>633</td>\n",
       "      <td>22.75%</td>\n",
       "      <td>77.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>653</td>\n",
       "      <td>177</td>\n",
       "      <td>830</td>\n",
       "      <td>21.33%</td>\n",
       "      <td>78.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>438</td>\n",
       "      <td>111</td>\n",
       "      <td>549</td>\n",
       "      <td>20.22%</td>\n",
       "      <td>79.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>755</td>\n",
       "      <td>178</td>\n",
       "      <td>933</td>\n",
       "      <td>19.08%</td>\n",
       "      <td>80.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>628</td>\n",
       "      <td>236</td>\n",
       "      <td>864</td>\n",
       "      <td>27.31%</td>\n",
       "      <td>72.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>686</td>\n",
       "      <td>309</td>\n",
       "      <td>995</td>\n",
       "      <td>31.06%</td>\n",
       "      <td>68.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>472</td>\n",
       "      <td>216</td>\n",
       "      <td>688</td>\n",
       "      <td>31.4%</td>\n",
       "      <td>68.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>237</td>\n",
       "      <td>229</td>\n",
       "      <td>466</td>\n",
       "      <td>49.14%</td>\n",
       "      <td>50.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>237</td>\n",
       "      <td>221</td>\n",
       "      <td>458</td>\n",
       "      <td>48.25%</td>\n",
       "      <td>51.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>138</td>\n",
       "      <td>124</td>\n",
       "      <td>262</td>\n",
       "      <td>47.33%</td>\n",
       "      <td>52.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10</th>\n",
       "      <td>361</td>\n",
       "      <td>297</td>\n",
       "      <td>658</td>\n",
       "      <td>45.14%</td>\n",
       "      <td>54.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11</th>\n",
       "      <td>417</td>\n",
       "      <td>350</td>\n",
       "      <td>767</td>\n",
       "      <td>45.63%</td>\n",
       "      <td>54.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12</th>\n",
       "      <td>443</td>\n",
       "      <td>426</td>\n",
       "      <td>869</td>\n",
       "      <td>49.02%</td>\n",
       "      <td>50.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01</th>\n",
       "      <td>405</td>\n",
       "      <td>453</td>\n",
       "      <td>858</td>\n",
       "      <td>52.8%</td>\n",
       "      <td>47.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02</th>\n",
       "      <td>405</td>\n",
       "      <td>428</td>\n",
       "      <td>833</td>\n",
       "      <td>51.38%</td>\n",
       "      <td>48.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03</th>\n",
       "      <td>543</td>\n",
       "      <td>670</td>\n",
       "      <td>1213</td>\n",
       "      <td>55.23%</td>\n",
       "      <td>44.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04</th>\n",
       "      <td>489</td>\n",
       "      <td>739</td>\n",
       "      <td>1228</td>\n",
       "      <td>60.18%</td>\n",
       "      <td>39.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05</th>\n",
       "      <td>667</td>\n",
       "      <td>1258</td>\n",
       "      <td>1925</td>\n",
       "      <td>65.35%</td>\n",
       "      <td>34.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06</th>\n",
       "      <td>582</td>\n",
       "      <td>1102</td>\n",
       "      <td>1684</td>\n",
       "      <td>65.44%</td>\n",
       "      <td>34.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07</th>\n",
       "      <td>662</td>\n",
       "      <td>1342</td>\n",
       "      <td>2004</td>\n",
       "      <td>66.97%</td>\n",
       "      <td>33.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08</th>\n",
       "      <td>376</td>\n",
       "      <td>832</td>\n",
       "      <td>1208</td>\n",
       "      <td>68.87%</td>\n",
       "      <td>31.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>637</td>\n",
       "      <td>1953</td>\n",
       "      <td>2590</td>\n",
       "      <td>75.41%</td>\n",
       "      <td>24.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>665</td>\n",
       "      <td>2221</td>\n",
       "      <td>2886</td>\n",
       "      <td>76.96%</td>\n",
       "      <td>23.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>459</td>\n",
       "      <td>1541</td>\n",
       "      <td>2000</td>\n",
       "      <td>77.05%</td>\n",
       "      <td>22.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>325</td>\n",
       "      <td>1302</td>\n",
       "      <td>1627</td>\n",
       "      <td>80.02%</td>\n",
       "      <td>19.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>468</td>\n",
       "      <td>1932</td>\n",
       "      <td>2400</td>\n",
       "      <td>80.5%</td>\n",
       "      <td>19.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>404</td>\n",
       "      <td>1861</td>\n",
       "      <td>2265</td>\n",
       "      <td>82.16%</td>\n",
       "      <td>17.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04</th>\n",
       "      <td>472</td>\n",
       "      <td>2570</td>\n",
       "      <td>3042</td>\n",
       "      <td>84.48%</td>\n",
       "      <td>15.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>432</td>\n",
       "      <td>2455</td>\n",
       "      <td>2887</td>\n",
       "      <td>85.04%</td>\n",
       "      <td>14.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>478</td>\n",
       "      <td>2562</td>\n",
       "      <td>3040</td>\n",
       "      <td>84.28%</td>\n",
       "      <td>15.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>318</td>\n",
       "      <td>1503</td>\n",
       "      <td>1821</td>\n",
       "      <td>82.54%</td>\n",
       "      <td>17.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>318</td>\n",
       "      <td>1557</td>\n",
       "      <td>1875</td>\n",
       "      <td>83.04%</td>\n",
       "      <td>16.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>395</td>\n",
       "      <td>2220</td>\n",
       "      <td>2615</td>\n",
       "      <td>84.89%</td>\n",
       "      <td>15.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>363</td>\n",
       "      <td>2211</td>\n",
       "      <td>2574</td>\n",
       "      <td>85.9%</td>\n",
       "      <td>14.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>323</td>\n",
       "      <td>1623</td>\n",
       "      <td>1946</td>\n",
       "      <td>83.4%</td>\n",
       "      <td>16.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>216</td>\n",
       "      <td>1307</td>\n",
       "      <td>1523</td>\n",
       "      <td>85.82%</td>\n",
       "      <td>14.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>135</td>\n",
       "      <td>1006</td>\n",
       "      <td>1141</td>\n",
       "      <td>88.17%</td>\n",
       "      <td>11.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>230</td>\n",
       "      <td>1297</td>\n",
       "      <td>1527</td>\n",
       "      <td>84.94%</td>\n",
       "      <td>15.06%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_modified  False  True  total modified_p normal_p\n",
       "year_month                                         \n",
       "2018-07        489   144    633     22.75%   77.25%\n",
       "2018-08        653   177    830     21.33%   78.67%\n",
       "2018-09        438   111    549     20.22%   79.78%\n",
       "2018-10        755   178    933     19.08%   80.92%\n",
       "2018-11        628   236    864     27.31%   72.69%\n",
       "2018-12        686   309    995     31.06%   68.94%\n",
       "2019-01        472   216    688      31.4%    68.6%\n",
       "2019-02        237   229    466     49.14%   50.86%\n",
       "2019-03        237   221    458     48.25%   51.75%\n",
       "2019-04        138   124    262     47.33%   52.67%\n",
       "2019-10        361   297    658     45.14%   54.86%\n",
       "2019-11        417   350    767     45.63%   54.37%\n",
       "2019-12        443   426    869     49.02%   50.98%\n",
       "2020-01        405   453    858      52.8%    47.2%\n",
       "2020-02        405   428    833     51.38%   48.62%\n",
       "2020-03        543   670   1213     55.23%   44.77%\n",
       "2020-04        489   739   1228     60.18%   39.82%\n",
       "2020-05        667  1258   1925     65.35%   34.65%\n",
       "2020-06        582  1102   1684     65.44%   34.56%\n",
       "2020-07        662  1342   2004     66.97%   33.03%\n",
       "2020-08        376   832   1208     68.87%   31.13%\n",
       "2020-10        637  1953   2590     75.41%   24.59%\n",
       "2020-11        665  2221   2886     76.96%   23.04%\n",
       "2020-12        459  1541   2000     77.05%   22.95%\n",
       "2021-01        325  1302   1627     80.02%   19.98%\n",
       "2021-02        468  1932   2400      80.5%    19.5%\n",
       "2021-03        404  1861   2265     82.16%   17.84%\n",
       "2021-04        472  2570   3042     84.48%   15.52%\n",
       "2021-05        432  2455   2887     85.04%   14.96%\n",
       "2021-06        478  2562   3040     84.28%   15.72%\n",
       "2021-07        318  1503   1821     82.54%   17.46%\n",
       "2021-08        318  1557   1875     83.04%   16.96%\n",
       "2021-09        395  2220   2615     84.89%   15.11%\n",
       "2021-10        363  2211   2574      85.9%    14.1%\n",
       "2021-11        323  1623   1946      83.4%    16.6%\n",
       "2021-12        216  1307   1523     85.82%   14.18%\n",
       "2022-01        135  1006   1141     88.17%   11.83%\n",
       "2022-02        230  1297   1527     84.94%   15.06%"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique the beacons per month\n",
    "dd = df.drop_duplicates(['md5', 'year_month'], keep='last')\n",
    "\n",
    "# group by year_month and is_modified\n",
    "mod_per_month = dd.groupby([\"year_month\", \"is_modified\"]).size().unstack()\n",
    "mod_per_month = mod_per_month.reset_index().set_index(\"year_month\")\n",
    "\n",
    "# Add totals and percentages\n",
    "mod_per_month[\"total\"] = (mod_per_month[True] + mod_per_month[False])\n",
    "mod_per_month[\"modified_p\"] = (mod_per_month[True] / mod_per_month[\"total\"] * 100).round(2).astype(str) + '%'\n",
    "mod_per_month[\"normal_p\"] = (mod_per_month[False] / mod_per_month[\"total\"] * 100).round(2).astype(str) + '%'\n",
    "mod_per_month\n",
    "# mod_per_month.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964cf127-441b-4b3e-8472-bed8faf18b0f",
   "metadata": {},
   "source": [
    "## Find `first_modified` date\n",
    "\n",
    "Now that we can identify if a beacon `is_modified` or not, we can determine when the first *modified* beacon appeared for a specific Cobalt Strike version.\n",
    "The delta between `first_seen` and `first_modified` can indicate how long it took for a version to become leaked and pirated.\n",
    "\n",
    "Note that we also do a check if `enum_version` is higher than `pe_version`, to find differences in the \"beacon payload\" and the \"team server\". A good example is `Cobalt Strike 4.5 (Dec 14, 2021)`, there are some beacons that have the `pe_export_stamp` of 4.4 but the enum version is 4.5. This indicates that a \"cobaltstrike.jar\" of version 4.5 is modified/cracked, but not properly as it uses encrypted resources (sleeves) from the previous 4.4 release. Most likely because they did not have the proper authorization (license) file to decrypt the sleeved resources, which includes the beacon.dll payload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b91e82e8-aac3-458e-bc68-292ec43d8427",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "from dissect.cobaltstrike.version import BeaconVersion\n",
    "\n",
    "MODIFIED_THRESHOLD = 2\n",
    "normal_version_to_first_seen = collections.defaultdict(list)\n",
    "modified_version_to_first_seen = collections.defaultdict(list)\n",
    "\n",
    "# Map the first seen date for every beacon version (normal and modified)\n",
    "for index, row in df.drop_duplicates([\"md5\"], keep=\"first\").iterrows():\n",
    "    bconfig = row[\"bconfig\"]\n",
    "    \n",
    "    pe_version = BeaconVersion.from_pe_export_stamp(bconfig.pe_export_stamp)\n",
    "    enum_version = BeaconVersion.from_max_setting_enum(bconfig.max_setting_enum)\n",
    "    \n",
    "    version = pe_version\n",
    "    if enum_version.tuple and enum_version.tuple > pe_version.tuple:\n",
    "        version = enum_version\n",
    "\n",
    "    dt = row[\"collected_dt\"]\n",
    "    modified = row[\"modifications\"] != \"normal\"\n",
    "\n",
    "    if modified:\n",
    "        modified_version_to_first_seen[version].append(dt)\n",
    "    else:\n",
    "        normal_version_to_first_seen[version].append(dt)\n",
    "\n",
    "# Now check for a given day how many `modified` beacons we have, if this is more\n",
    "# than MODIFIED_THRESHOLD, we note that day as the `first modified` date.\n",
    "version_stats = []\n",
    "for version, normal_dts in sorted(normal_version_to_first_seen.items(), key=lambda x: x[0].tuple):\n",
    "    first_seen = min(normal_dts)\n",
    "    day_to_dt = collections.defaultdict(list)\n",
    "    modified_dts = modified_version_to_first_seen[version]\n",
    "    for modified_dt in modified_version_to_first_seen[version]:\n",
    "        day_key = modified_dt.strftime(\"%Y-%m-%d\")\n",
    "        day_to_dt[day_key].append(modified_dt)\n",
    "\n",
    "    first_seen_modified = None\n",
    "    for day, dts in sorted(day_to_dt.items()):\n",
    "        # If we see modified amount > MODIFIED_THRESHOLD, we consider it\n",
    "        # the first date we see in it `modified` in the wild.\n",
    "        if len(dts) > MODIFIED_THRESHOLD:\n",
    "            first_seen_modified = min(dts)\n",
    "            break\n",
    "\n",
    "    delta = None\n",
    "    if first_seen_modified:\n",
    "        delta = first_seen_modified - first_seen\n",
    "    normal_count = len(normal_dts)\n",
    "    modified_count = len(modified_dts)\n",
    "    total_count = normal_count + modified_count\n",
    "    version_stats.append([version, version.date, first_seen, first_seen_modified, delta, total_count, normal_count, modified_count])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c3fa311-2bf1-46d6-bf20-24d8b727ef60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>version</th>\n",
       "      <th>version_date</th>\n",
       "      <th>first_seen</th>\n",
       "      <th>first_modified</th>\n",
       "      <th>delta</th>\n",
       "      <th>total_count</th>\n",
       "      <th>normal_count</th>\n",
       "      <th>modified_count</th>\n",
       "      <th>modified_p</th>\n",
       "      <th>normal_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cobalt Strike 4.0 (Dec 05, 2019)</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>112 days</td>\n",
       "      <td>9443</td>\n",
       "      <td>1319</td>\n",
       "      <td>8124</td>\n",
       "      <td>86.03%</td>\n",
       "      <td>13.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cobalt Strike 4.1 (Jun 25, 2020)</td>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>2020-07-28</td>\n",
       "      <td>33 days</td>\n",
       "      <td>5146</td>\n",
       "      <td>994</td>\n",
       "      <td>4152</td>\n",
       "      <td>80.68%</td>\n",
       "      <td>19.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cobalt Strike 4.2 (Nov 06, 2020)</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>33 days</td>\n",
       "      <td>4876</td>\n",
       "      <td>739</td>\n",
       "      <td>4137</td>\n",
       "      <td>84.84%</td>\n",
       "      <td>15.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cobalt Strike 4.3 (Mar 03, 2021)</td>\n",
       "      <td>2021-03-03</td>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>28 days</td>\n",
       "      <td>4810</td>\n",
       "      <td>1081</td>\n",
       "      <td>3729</td>\n",
       "      <td>77.53%</td>\n",
       "      <td>22.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cobalt Strike 4.4 (Aug 04, 2021)</td>\n",
       "      <td>2021-08-04</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>2021-08-24</td>\n",
       "      <td>13 days</td>\n",
       "      <td>1783</td>\n",
       "      <td>626</td>\n",
       "      <td>1157</td>\n",
       "      <td>64.89%</td>\n",
       "      <td>35.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cobalt Strike 4.5 (Dec 14, 2021)</td>\n",
       "      <td>2021-12-14</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>58 days</td>\n",
       "      <td>189</td>\n",
       "      <td>183</td>\n",
       "      <td>6</td>\n",
       "      <td>3.17%</td>\n",
       "      <td>96.83%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             version version_date first_seen first_modified  \\\n",
       "13  Cobalt Strike 4.0 (Dec 05, 2019)   2019-12-05 2019-10-23     2020-02-12   \n",
       "14  Cobalt Strike 4.1 (Jun 25, 2020)   2020-06-25 2020-06-25     2020-07-28   \n",
       "15  Cobalt Strike 4.2 (Nov 06, 2020)   2020-11-06 2020-10-25     2020-11-27   \n",
       "16  Cobalt Strike 4.3 (Mar 03, 2021)   2021-03-03 2021-03-05     2021-04-02   \n",
       "17  Cobalt Strike 4.4 (Aug 04, 2021)   2021-08-04 2021-08-11     2021-08-24   \n",
       "18  Cobalt Strike 4.5 (Dec 14, 2021)   2021-12-14 2021-12-20     2022-02-16   \n",
       "\n",
       "      delta  total_count  normal_count  modified_count modified_p normal_p  \n",
       "13 112 days         9443          1319            8124     86.03%   13.97%  \n",
       "14  33 days         5146           994            4152     80.68%   19.32%  \n",
       "15  33 days         4876           739            4137     84.84%   15.16%  \n",
       "16  28 days         4810          1081            3729     77.53%   22.47%  \n",
       "17  13 days         1783           626            1157     64.89%   35.11%  \n",
       "18  58 days          189           183               6      3.17%   96.83%  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe holding the delta and version statistics\n",
    "version_df = pd.DataFrame(version_stats, columns=[\"version\", \"version_date\", \"first_seen\", \"first_modified\", \"delta\", \"total_count\", \"normal_count\", \"modified_count\"])\n",
    "\n",
    "# Add some percentages\n",
    "version_df[\"modified_p\"] = ((version_df[\"modified_count\"] / version_df[\"total_count\"]) * 100).round(2).astype(str) + \"%\"\n",
    "version_df[\"normal_p\"] = ((version_df[\"normal_count\"] / version_df[\"total_count\"]) * 100).round(2).astype(str) + \"%\"\n",
    "\n",
    "# Only show versions > 4.0\n",
    "version_df[version_df.version > \"Cobalt Strike 4.0\"].sort_values([\"version_date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ff4026-c96e-4721-85db-6cc814d41983",
   "metadata": {},
   "source": [
    "## Modified Beacon overview\n",
    "Matrix with different modification counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "53c0c7ba-9eee-4402-b142-67ed9fbc9d20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>modifications</th>\n",
       "      <th>bad_watermark</th>\n",
       "      <th>bad_watermark+reused_watermark</th>\n",
       "      <th>bad_watermark+reused_watermark+bad_watermarkhash</th>\n",
       "      <th>bad_watermark+reused_watermark+reused_pubkey</th>\n",
       "      <th>modified_trial</th>\n",
       "      <th>modified_trial+bad_watermark</th>\n",
       "      <th>modified_trial+bad_watermark+reused_watermark</th>\n",
       "      <th>modified_trial+reused_watermark</th>\n",
       "      <th>modified_xorkey</th>\n",
       "      <th>modified_xorkey+bad_watermark+reused_watermark</th>\n",
       "      <th>modified_xorkey+bad_watermark+reused_watermark+reused_pubkey</th>\n",
       "      <th>modified_xorkey+reused_watermark</th>\n",
       "      <th>modified_xorkey+reused_watermark+reused_pubkey</th>\n",
       "      <th>normal</th>\n",
       "      <th>reused_pubkey</th>\n",
       "      <th>reused_watermark</th>\n",
       "      <th>reused_watermark+reused_pubkey</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07</th>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1382</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1535</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1524</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>0</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>296</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>208</td>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>551</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>551</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10</th>\n",
       "      <td>0</td>\n",
       "      <td>521</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1152</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11</th>\n",
       "      <td>0</td>\n",
       "      <td>553</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1040</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12</th>\n",
       "      <td>0</td>\n",
       "      <td>810</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1518</td>\n",
       "      <td>0</td>\n",
       "      <td>338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1258</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1908</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02</th>\n",
       "      <td>0</td>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1114</td>\n",
       "      <td>0</td>\n",
       "      <td>319</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03</th>\n",
       "      <td>0</td>\n",
       "      <td>915</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1711</td>\n",
       "      <td>0</td>\n",
       "      <td>853</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04</th>\n",
       "      <td>3</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05</th>\n",
       "      <td>0</td>\n",
       "      <td>807</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1613</td>\n",
       "      <td>0</td>\n",
       "      <td>1156</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06</th>\n",
       "      <td>0</td>\n",
       "      <td>832</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2040</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07</th>\n",
       "      <td>0</td>\n",
       "      <td>454</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1254</td>\n",
       "      <td>0</td>\n",
       "      <td>1310</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08</th>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>418</td>\n",
       "      <td>0</td>\n",
       "      <td>509</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>6</td>\n",
       "      <td>1225</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1264</td>\n",
       "      <td>0</td>\n",
       "      <td>1677</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>7</td>\n",
       "      <td>1835</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1664</td>\n",
       "      <td>0</td>\n",
       "      <td>2373</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>4</td>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>9</td>\n",
       "      <td>576</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>571</td>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>7</td>\n",
       "      <td>1145</td>\n",
       "      <td>0</td>\n",
       "      <td>456</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1314</td>\n",
       "      <td>0</td>\n",
       "      <td>2827</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>6</td>\n",
       "      <td>968</td>\n",
       "      <td>0</td>\n",
       "      <td>409</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>1094</td>\n",
       "      <td>2</td>\n",
       "      <td>2745</td>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04</th>\n",
       "      <td>0</td>\n",
       "      <td>1108</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1098</td>\n",
       "      <td>2</td>\n",
       "      <td>3111</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>0</td>\n",
       "      <td>1123</td>\n",
       "      <td>0</td>\n",
       "      <td>672</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1107</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>0</td>\n",
       "      <td>1354</td>\n",
       "      <td>0</td>\n",
       "      <td>910</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>1386</td>\n",
       "      <td>39</td>\n",
       "      <td>3266</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>1</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>541</td>\n",
       "      <td>12</td>\n",
       "      <td>1212</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>2</td>\n",
       "      <td>688</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>524</td>\n",
       "      <td>12</td>\n",
       "      <td>1141</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>6</td>\n",
       "      <td>1482</td>\n",
       "      <td>0</td>\n",
       "      <td>534</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>992</td>\n",
       "      <td>26</td>\n",
       "      <td>2510</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>452</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>859</td>\n",
       "      <td>29</td>\n",
       "      <td>2459</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>2</td>\n",
       "      <td>690</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>6</td>\n",
       "      <td>1401</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>0</td>\n",
       "      <td>482</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>406</td>\n",
       "      <td>6</td>\n",
       "      <td>1106</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>0</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>3</td>\n",
       "      <td>629</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>2</td>\n",
       "      <td>703</td>\n",
       "      <td>13</td>\n",
       "      <td>305</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>1570</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "modifications  bad_watermark  bad_watermark+reused_watermark  \\\n",
       "year_month                                                     \n",
       "2018-07                    0                              42   \n",
       "2018-08                    0                              58   \n",
       "2018-09                    0                              18   \n",
       "2018-10                    0                              54   \n",
       "2018-11                    0                             168   \n",
       "2018-12                    0                             268   \n",
       "2019-01                    0                             256   \n",
       "2019-02                    0                             182   \n",
       "2019-03                    0                             168   \n",
       "2019-04                    0                              84   \n",
       "2019-10                    0                             521   \n",
       "2019-11                    0                             553   \n",
       "2019-12                    0                             810   \n",
       "2020-01                    0                            1258   \n",
       "2020-02                    0                             587   \n",
       "2020-03                    0                             915   \n",
       "2020-04                    3                             460   \n",
       "2020-05                    0                             807   \n",
       "2020-06                    0                             832   \n",
       "2020-07                    0                             454   \n",
       "2020-08                    0                             162   \n",
       "2020-10                    6                            1225   \n",
       "2020-11                    7                            1835   \n",
       "2020-12                    4                             769   \n",
       "2021-01                    9                             576   \n",
       "2021-02                    7                            1145   \n",
       "2021-03                    6                             968   \n",
       "2021-04                    0                            1108   \n",
       "2021-05                    0                            1123   \n",
       "2021-06                    0                            1354   \n",
       "2021-07                    1                             529   \n",
       "2021-08                    2                             688   \n",
       "2021-09                    6                            1482   \n",
       "2021-10                    0                            1305   \n",
       "2021-11                    2                             690   \n",
       "2021-12                    0                             482   \n",
       "2022-01                    0                             274   \n",
       "2022-02                    2                             703   \n",
       "\n",
       "modifications  bad_watermark+reused_watermark+bad_watermarkhash  \\\n",
       "year_month                                                        \n",
       "2018-07                                                       0   \n",
       "2018-08                                                       0   \n",
       "2018-09                                                       0   \n",
       "2018-10                                                       0   \n",
       "2018-11                                                       0   \n",
       "2018-12                                                       0   \n",
       "2019-01                                                       0   \n",
       "2019-02                                                       0   \n",
       "2019-03                                                       0   \n",
       "2019-04                                                       0   \n",
       "2019-10                                                       0   \n",
       "2019-11                                                       0   \n",
       "2019-12                                                       0   \n",
       "2020-01                                                       0   \n",
       "2020-02                                                       0   \n",
       "2020-03                                                       0   \n",
       "2020-04                                                       0   \n",
       "2020-05                                                       0   \n",
       "2020-06                                                       0   \n",
       "2020-07                                                       0   \n",
       "2020-08                                                       0   \n",
       "2020-10                                                       0   \n",
       "2020-11                                                       0   \n",
       "2020-12                                                       0   \n",
       "2021-01                                                       0   \n",
       "2021-02                                                       0   \n",
       "2021-03                                                       0   \n",
       "2021-04                                                       0   \n",
       "2021-05                                                       0   \n",
       "2021-06                                                       0   \n",
       "2021-07                                                       0   \n",
       "2021-08                                                       0   \n",
       "2021-09                                                       0   \n",
       "2021-10                                                       0   \n",
       "2021-11                                                       0   \n",
       "2021-12                                                       0   \n",
       "2022-01                                                       0   \n",
       "2022-02                                                      13   \n",
       "\n",
       "modifications  bad_watermark+reused_watermark+reused_pubkey  modified_trial  \\\n",
       "year_month                                                                    \n",
       "2018-07                                                   0             281   \n",
       "2018-08                                                   0             308   \n",
       "2018-09                                                   0             101   \n",
       "2018-10                                                   0             282   \n",
       "2018-11                                                   0             231   \n",
       "2018-12                                                   0             296   \n",
       "2019-01                                                   0             208   \n",
       "2019-02                                                   0             218   \n",
       "2019-03                                                   0             252   \n",
       "2019-04                                                   0              90   \n",
       "2019-10                                                   4               3   \n",
       "2019-11                                                   3               0   \n",
       "2019-12                                                   6               0   \n",
       "2020-01                                                   6              10   \n",
       "2020-02                                                   4               0   \n",
       "2020-03                                                   0               0   \n",
       "2020-04                                                   0               3   \n",
       "2020-05                                                   7              20   \n",
       "2020-06                                                   8              22   \n",
       "2020-07                                                   7               5   \n",
       "2020-08                                                   3               4   \n",
       "2020-10                                                  30               6   \n",
       "2020-11                                                  73               4   \n",
       "2020-12                                                 159               3   \n",
       "2021-01                                                 154               4   \n",
       "2021-02                                                 456               8   \n",
       "2021-03                                                 409               2   \n",
       "2021-04                                                 535               6   \n",
       "2021-05                                                 672               4   \n",
       "2021-06                                                 910               6   \n",
       "2021-07                                                 339               4   \n",
       "2021-08                                                 324               2   \n",
       "2021-09                                                 534               4   \n",
       "2021-10                                                 452               3   \n",
       "2021-11                                                 222               0   \n",
       "2021-12                                                 188               0   \n",
       "2022-01                                                 101               0   \n",
       "2022-02                                                 305               0   \n",
       "\n",
       "modifications  modified_trial+bad_watermark  \\\n",
       "year_month                                    \n",
       "2018-07                                   0   \n",
       "2018-08                                   0   \n",
       "2018-09                                   0   \n",
       "2018-10                                   0   \n",
       "2018-11                                   0   \n",
       "2018-12                                   3   \n",
       "2019-01                                   3   \n",
       "2019-02                                   2   \n",
       "2019-03                                   1   \n",
       "2019-04                                   0   \n",
       "2019-10                                   0   \n",
       "2019-11                                   0   \n",
       "2019-12                                   0   \n",
       "2020-01                                   0   \n",
       "2020-02                                   0   \n",
       "2020-03                                   0   \n",
       "2020-04                                   0   \n",
       "2020-05                                   0   \n",
       "2020-06                                   0   \n",
       "2020-07                                   0   \n",
       "2020-08                                   0   \n",
       "2020-10                                   0   \n",
       "2020-11                                   0   \n",
       "2020-12                                   0   \n",
       "2021-01                                   0   \n",
       "2021-02                                   0   \n",
       "2021-03                                   0   \n",
       "2021-04                                   0   \n",
       "2021-05                                   0   \n",
       "2021-06                                   0   \n",
       "2021-07                                   0   \n",
       "2021-08                                   0   \n",
       "2021-09                                   0   \n",
       "2021-10                                   0   \n",
       "2021-11                                   0   \n",
       "2021-12                                   0   \n",
       "2022-01                                   0   \n",
       "2022-02                                   0   \n",
       "\n",
       "modifications  modified_trial+bad_watermark+reused_watermark  \\\n",
       "year_month                                                     \n",
       "2018-07                                                    0   \n",
       "2018-08                                                   15   \n",
       "2018-09                                                    7   \n",
       "2018-10                                                   35   \n",
       "2018-11                                                  136   \n",
       "2018-12                                                  204   \n",
       "2019-01                                                  149   \n",
       "2019-02                                                   98   \n",
       "2019-03                                                   60   \n",
       "2019-04                                                   17   \n",
       "2019-10                                                   22   \n",
       "2019-11                                                   23   \n",
       "2019-12                                                   72   \n",
       "2020-01                                                  104   \n",
       "2020-02                                                   38   \n",
       "2020-03                                                   52   \n",
       "2020-04                                                   34   \n",
       "2020-05                                                   70   \n",
       "2020-06                                                  127   \n",
       "2020-07                                                   62   \n",
       "2020-08                                                   18   \n",
       "2020-10                                                   25   \n",
       "2020-11                                                   27   \n",
       "2020-12                                                   12   \n",
       "2021-01                                                   12   \n",
       "2021-02                                                   29   \n",
       "2021-03                                                   29   \n",
       "2021-04                                                   22   \n",
       "2021-05                                                   23   \n",
       "2021-06                                                   20   \n",
       "2021-07                                                    6   \n",
       "2021-08                                                    6   \n",
       "2021-09                                                    8   \n",
       "2021-10                                                    8   \n",
       "2021-11                                                    4   \n",
       "2021-12                                                    3   \n",
       "2022-01                                                    3   \n",
       "2022-02                                                    5   \n",
       "\n",
       "modifications  modified_trial+reused_watermark  modified_xorkey  \\\n",
       "year_month                                                        \n",
       "2018-07                                      0                0   \n",
       "2018-08                                      0                0   \n",
       "2018-09                                      0                0   \n",
       "2018-10                                      0                0   \n",
       "2018-11                                      0                0   \n",
       "2018-12                                      0                0   \n",
       "2019-01                                      0                0   \n",
       "2019-02                                      0                0   \n",
       "2019-03                                      0                0   \n",
       "2019-04                                      0                0   \n",
       "2019-10                                     35                0   \n",
       "2019-11                                     19                0   \n",
       "2019-12                                     24                0   \n",
       "2020-01                                     10                0   \n",
       "2020-02                                     13                0   \n",
       "2020-03                                     10                0   \n",
       "2020-04                                      6                0   \n",
       "2020-05                                     15                0   \n",
       "2020-06                                     34                0   \n",
       "2020-07                                     22                0   \n",
       "2020-08                                      9                0   \n",
       "2020-10                                     14                0   \n",
       "2020-11                                     21                0   \n",
       "2020-12                                      8                1   \n",
       "2021-01                                      5                0   \n",
       "2021-02                                     10                1   \n",
       "2021-03                                      3                2   \n",
       "2021-04                                      0                1   \n",
       "2021-05                                      0                0   \n",
       "2021-06                                      0                0   \n",
       "2021-07                                      0                0   \n",
       "2021-08                                      0                1   \n",
       "2021-09                                      0                0   \n",
       "2021-10                                      0                1   \n",
       "2021-11                                      0                2   \n",
       "2021-12                                      0                0   \n",
       "2022-01                                      0                0   \n",
       "2022-02                                      0                4   \n",
       "\n",
       "modifications  modified_xorkey+bad_watermark+reused_watermark  \\\n",
       "year_month                                                      \n",
       "2018-07                                                     0   \n",
       "2018-08                                                     0   \n",
       "2018-09                                                     0   \n",
       "2018-10                                                     0   \n",
       "2018-11                                                     0   \n",
       "2018-12                                                     0   \n",
       "2019-01                                                     0   \n",
       "2019-02                                                     0   \n",
       "2019-03                                                     0   \n",
       "2019-04                                                     0   \n",
       "2019-10                                                     0   \n",
       "2019-11                                                     0   \n",
       "2019-12                                                     0   \n",
       "2020-01                                                     0   \n",
       "2020-02                                                     0   \n",
       "2020-03                                                     0   \n",
       "2020-04                                                     0   \n",
       "2020-05                                                     0   \n",
       "2020-06                                                     0   \n",
       "2020-07                                                     0   \n",
       "2020-08                                                     0   \n",
       "2020-10                                                     0   \n",
       "2020-11                                                     0   \n",
       "2020-12                                                     2   \n",
       "2021-01                                                     3   \n",
       "2021-02                                                     6   \n",
       "2021-03                                                     1   \n",
       "2021-04                                                     6   \n",
       "2021-05                                                    10   \n",
       "2021-06                                                    22   \n",
       "2021-07                                                    15   \n",
       "2021-08                                                    14   \n",
       "2021-09                                                    14   \n",
       "2021-10                                                    21   \n",
       "2021-11                                                    19   \n",
       "2021-12                                                     9   \n",
       "2022-01                                                     4   \n",
       "2022-02                                                    14   \n",
       "\n",
       "modifications  modified_xorkey+bad_watermark+reused_watermark+reused_pubkey  \\\n",
       "year_month                                                                    \n",
       "2018-07                                                        0              \n",
       "2018-08                                                        0              \n",
       "2018-09                                                        0              \n",
       "2018-10                                                        0              \n",
       "2018-11                                                        0              \n",
       "2018-12                                                        0              \n",
       "2019-01                                                        0              \n",
       "2019-02                                                        0              \n",
       "2019-03                                                        0              \n",
       "2019-04                                                        0              \n",
       "2019-10                                                        0              \n",
       "2019-11                                                        0              \n",
       "2019-12                                                        0              \n",
       "2020-01                                                        0              \n",
       "2020-02                                                        0              \n",
       "2020-03                                                        0              \n",
       "2020-04                                                        0              \n",
       "2020-05                                                        0              \n",
       "2020-06                                                        0              \n",
       "2020-07                                                        0              \n",
       "2020-08                                                        0              \n",
       "2020-10                                                        0              \n",
       "2020-11                                                        0              \n",
       "2020-12                                                        0              \n",
       "2021-01                                                        0              \n",
       "2021-02                                                        4              \n",
       "2021-03                                                        1              \n",
       "2021-04                                                        4              \n",
       "2021-05                                                        2              \n",
       "2021-06                                                        8              \n",
       "2021-07                                                        3              \n",
       "2021-08                                                        2              \n",
       "2021-09                                                        4              \n",
       "2021-10                                                        4              \n",
       "2021-11                                                        3              \n",
       "2021-12                                                        3              \n",
       "2022-01                                                        1              \n",
       "2022-02                                                        4              \n",
       "\n",
       "modifications  modified_xorkey+reused_watermark  \\\n",
       "year_month                                        \n",
       "2018-07                                       0   \n",
       "2018-08                                       0   \n",
       "2018-09                                       0   \n",
       "2018-10                                       0   \n",
       "2018-11                                       0   \n",
       "2018-12                                       0   \n",
       "2019-01                                       0   \n",
       "2019-02                                       0   \n",
       "2019-03                                       0   \n",
       "2019-04                                       0   \n",
       "2019-10                                       0   \n",
       "2019-11                                       0   \n",
       "2019-12                                       0   \n",
       "2020-01                                       0   \n",
       "2020-02                                       0   \n",
       "2020-03                                       0   \n",
       "2020-04                                       0   \n",
       "2020-05                                       0   \n",
       "2020-06                                       0   \n",
       "2020-07                                       0   \n",
       "2020-08                                       0   \n",
       "2020-10                                       0   \n",
       "2020-11                                       0   \n",
       "2020-12                                       1   \n",
       "2021-01                                       4   \n",
       "2021-02                                      15   \n",
       "2021-03                                      18   \n",
       "2021-04                                      23   \n",
       "2021-05                                      40   \n",
       "2021-06                                      55   \n",
       "2021-07                                       9   \n",
       "2021-08                                      15   \n",
       "2021-09                                      18   \n",
       "2021-10                                      18   \n",
       "2021-11                                      17   \n",
       "2021-12                                      16   \n",
       "2022-01                                      11   \n",
       "2022-02                                      29   \n",
       "\n",
       "modifications  modified_xorkey+reused_watermark+reused_pubkey  normal  \\\n",
       "year_month                                                              \n",
       "2018-07                                                     0    1147   \n",
       "2018-08                                                     0    1382   \n",
       "2018-09                                                     0     485   \n",
       "2018-10                                                     0    1535   \n",
       "2018-11                                                     0    1524   \n",
       "2018-12                                                     0    1934   \n",
       "2019-01                                                     0    1362   \n",
       "2019-02                                                     0     551   \n",
       "2019-03                                                     0     551   \n",
       "2019-04                                                     0     217   \n",
       "2019-10                                                     0    1152   \n",
       "2019-11                                                     0    1040   \n",
       "2019-12                                                     0    1518   \n",
       "2020-01                                                     0    1908   \n",
       "2020-02                                                     0    1114   \n",
       "2020-03                                                     0    1711   \n",
       "2020-04                                                     0     852   \n",
       "2020-05                                                     0    1613   \n",
       "2020-06                                                     0    2040   \n",
       "2020-07                                                     0    1254   \n",
       "2020-08                                                     0     418   \n",
       "2020-10                                                     0    1264   \n",
       "2020-11                                                     0    1664   \n",
       "2020-12                                                     0     738   \n",
       "2021-01                                                     2     571   \n",
       "2021-02                                                     0    1314   \n",
       "2021-03                                                    10    1094   \n",
       "2021-04                                                     1    1098   \n",
       "2021-05                                                     3    1107   \n",
       "2021-06                                                     5    1386   \n",
       "2021-07                                                     1     541   \n",
       "2021-08                                                     1     524   \n",
       "2021-09                                                     5     992   \n",
       "2021-10                                                     0     859   \n",
       "2021-11                                                     0     600   \n",
       "2021-12                                                     1     406   \n",
       "2022-01                                                     0     165   \n",
       "2022-02                                                     2     536   \n",
       "\n",
       "modifications  reused_pubkey  reused_watermark  reused_watermark+reused_pubkey  \n",
       "year_month                                                                      \n",
       "2018-07                    0                 0                               0  \n",
       "2018-08                    0                 0                               0  \n",
       "2018-09                    0                 0                               0  \n",
       "2018-10                    0                 0                               0  \n",
       "2018-11                    0                 0                               0  \n",
       "2018-12                    0                 0                               0  \n",
       "2019-01                    0                 0                               0  \n",
       "2019-02                    0                 0                               0  \n",
       "2019-03                    0                 0                               0  \n",
       "2019-04                    0                 0                               0  \n",
       "2019-10                    0               161                               0  \n",
       "2019-11                    0               200                               0  \n",
       "2019-12                    0               338                               0  \n",
       "2020-01                    0               490                               0  \n",
       "2020-02                    0               319                              56  \n",
       "2020-03                    0               853                             132  \n",
       "2020-04                    0               500                             171  \n",
       "2020-05                    0              1156                             390  \n",
       "2020-06                    0              1893                             661  \n",
       "2020-07                    0              1310                             441  \n",
       "2020-08                    0               509                             178  \n",
       "2020-10                    0              1677                             464  \n",
       "2020-11                    0              2373                             656  \n",
       "2020-12                    0              1109                             347  \n",
       "2021-01                    0               972                             294  \n",
       "2021-02                    0              2827                             945  \n",
       "2021-03                    2              2745                             779  \n",
       "2021-04                    2              3111                             803  \n",
       "2021-05                    1              2752                             790  \n",
       "2021-06                   39              3266                             908  \n",
       "2021-07                   12              1212                             275  \n",
       "2021-08                   12              1141                             260  \n",
       "2021-09                   26              2510                             681  \n",
       "2021-10                   29              2459                             724  \n",
       "2021-11                    6              1401                             475  \n",
       "2021-12                    6              1106                             461  \n",
       "2022-01                    3               629                             241  \n",
       "2022-02                    1              1570                             643  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"year_month\", \"modifications\"]).size().unstack().fillna(0).astype(int)\n",
    "# df.groupby([\"beacon_version\", \"modifications\"]).size().unstack().fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546a27c7-e8df-4dce-b11c-8bffa757747c",
   "metadata": {},
   "source": [
    "# PE artifacts statistics\n",
    "\n",
    "Overview of some extracted PE artifacts from the beacon payloads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "acc9e828-d663-42bf-90a4-63c4ae849904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb5f838b6fa840e8995966f44b221ed1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c87be798ab0f45fd839197f48d6d46e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84fc455793184bf18396095e37d17403",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72a56358ff7a417f8eb40b4aebbdc75d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def b64decode(stage):\n",
    "    return base64.b64decode(stage) if stage else stage\n",
    "\n",
    "# We need to base64 decode the data first\n",
    "df[\"stage_prepend_decoded\"] = df[\"stage_prepend\"].progress_apply(b64decode)\n",
    "df[\"stage_append_decoded\"] = df[\"stage_append\"].progress_apply(b64decode)\n",
    "df[\"magic_pe_decoded\"] = df[\"magic_pe\"].progress_apply(b64decode)\n",
    "df[\"magic_mz_decoded\"] = df[\"magic_mz\"].progress_apply(b64decode)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b343081a-023c-4656-ac6d-64c4d572bed2",
   "metadata": {},
   "source": [
    "## MZ header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42e09970-8551-4c9c-b1ed-d92384b3a032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87cee2332d9946f9a6d910fd26575d00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "422c6e6608214f949ce34d9116f9388d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb607632cb0d402b99afe5f1e3b2a51e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/128340 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>magic_mz_decoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'MZ'</td>\n",
       "      <td>23192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'MZRE'</td>\n",
       "      <td>11442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'MZREMZRE'</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'H@KC'</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'@H@H'</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b']U]U'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b'TG'</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b'MSBE'</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b'KCKC'</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b'MEME'</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b'REOL'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b'MEZR'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b'MCQQ'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b'NFIA'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b'REZM'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b'TGRE'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b'MZUS'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>b'ZRCE'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>b'AC'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>b'MTRE'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>b'MZAR'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>b'GOGO'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          index  magic_mz_decoded\n",
       "0         b'MZ'             23192\n",
       "1       b'MZRE'             11442\n",
       "2   b'MZREMZRE'                62\n",
       "3       b'H@KC'                25\n",
       "4       b'@H@H'                17\n",
       "5       b']U]U'                13\n",
       "6         b'TG'                12\n",
       "7       b'MSBE'                10\n",
       "8       b'KCKC'                 7\n",
       "9       b'MEME'                 7\n",
       "10      b'REOL'                 6\n",
       "11      b'MEZR'                 5\n",
       "12      b'MCQQ'                 5\n",
       "13      b'NFIA'                 4\n",
       "14      b'REZM'                 3\n",
       "15      b'TGRE'                 2\n",
       "16      b'MZUS'                 2\n",
       "17      b'ZRCE'                 2\n",
       "18        b'AC'                 2\n",
       "19      b'MTRE'                 1\n",
       "20      b'MZAR'                 1\n",
       "21      b'GOGO'                 1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_ascii(data):\n",
    "    return data.isascii() if data else False\n",
    "\n",
    "df[\"stage_prepend_is_ascii\"] = df[\"stage_prepend_decoded\"].progress_apply(is_ascii)\n",
    "df[\"magic_mz_is_ascii\"] = df[\"magic_mz_decoded\"].progress_apply(is_ascii)\n",
    "df[\"magic_pe_is_ascii\"] = df[\"magic_pe_decoded\"].progress_apply(is_ascii)\n",
    "\n",
    "df[df[\"magic_mz_is_ascii\"]].drop_duplicates([\"md5\"])[\"magic_mz_decoded\"].value_counts().reset_index()\n",
    "# print(df[df[\"magic_mz_is_ascii\"]].drop_duplicates([\"md5\"])[\"magic_mz_decoded\"].value_counts().to_csv())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e107ba-e99d-4c08-84c3-e71695e51ea2",
   "metadata": {},
   "source": [
    "## PE header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c6a9e0d7-c016-417e-8242-8a8f8245e445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>magic_pe_decoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'PE'</td>\n",
       "      <td>33517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'NO'</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'EA'</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'CE'</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'AS'</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b'SB'</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b'MO'</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b'SO'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b'ZE'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b'VF'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b'Mp'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b'RK'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b'GK'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b'BI'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b'VJ'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b'TB'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b'CA'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>b'sB'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>b'NV'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>b'CN'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  magic_pe_decoded\n",
       "0   b'PE'             33517\n",
       "1   b'NO'               959\n",
       "2   b'EA'               123\n",
       "3   b'CE'                11\n",
       "4   b'AS'                11\n",
       "5   b'SB'                10\n",
       "6   b'MO'                 8\n",
       "7   b'SO'                 6\n",
       "8   b'ZE'                 6\n",
       "9   b'VF'                 6\n",
       "10  b'Mp'                 5\n",
       "11  b'RK'                 5\n",
       "12  b'GK'                 5\n",
       "13  b'BI'                 5\n",
       "14  b'VJ'                 4\n",
       "15  b'TB'                 4\n",
       "16  b'CA'                 4\n",
       "17  b'sB'                 4\n",
       "18  b'NV'                 4\n",
       "19  b'CN'                 4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"magic_pe_is_ascii\"]].drop_duplicates([\"md5\"])[\"magic_pe_decoded\"].value_counts().reset_index().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f762aa-d195-491c-8881-e0d3600139c3",
   "metadata": {},
   "source": [
    "## Stage prepend\n",
    "\n",
    "Should be valid shellcode as this is prepended in front of the reflective beacon payload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a90116c0-37c8-4390-89ed-d580f8fc4cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stage_prepend_decoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'$$$$$'</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'@H@H@H@H'</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'AAAAAA'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'GOEMGODL'</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'ZRZR'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b'AA'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b'PPP'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b'GCGBOKJO'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b'CKCKRZCKAICKAI'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b'BBBBBB'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b'CBCBCB'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b'CCCCCCCCCCCCCCCCCCCCCCCCCC'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b'AAAAA'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b'B?AH/'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b'JFIFJFIF'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b'$]'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            index  stage_prepend_decoded\n",
       "0                        b'$$$$$'                     10\n",
       "1                     b'@H@H@H@H'                      8\n",
       "2                       b'AAAAAA'                      6\n",
       "3                     b'GOEMGODL'                      6\n",
       "4                         b'ZRZR'                      5\n",
       "5                           b'AA'                      4\n",
       "6                          b'PPP'                      4\n",
       "7                     b'GCGBOKJO'                      3\n",
       "8               b'CKCKRZCKAICKAI'                      2\n",
       "9                       b'BBBBBB'                      2\n",
       "10                      b'CBCBCB'                      2\n",
       "11  b'CCCCCCCCCCCCCCCCCCCCCCCCCC'                      2\n",
       "12                       b'AAAAA'                      1\n",
       "13                       b'B?AH/'                      1\n",
       "14                    b'JFIFJFIF'                      1\n",
       "15                          b'$]'                      1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepend_ascii_df = df[df[\"stage_prepend_is_ascii\"]].drop_duplicates([\"md5\"])[\"stage_prepend_decoded\"].value_counts()\n",
    "prepend_ascii_df.reset_index()\n",
    "# print(prepend_ascii_df.to_csv())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2b5ede-8784-48b6-9f5e-077f643baa31",
   "metadata": {},
   "source": [
    "## Stage append\n",
    "This is data appended to the beacon payload, it does not have to be valid shellcode. The *EICAR* string is inserted automatically for trial versions of Cobalt Strike."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "866c5a87-bb02-47b7-b4f7-00dadcd5b215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stage_append_decoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>32162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'X5O!P%@AP[4\\\\PZX54(P^)7CC)7}$EICAR-STANDARD-...</td>\n",
       "      <td>1098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'\\x90\\xd9\\xfa\\x90\\xd9\\xff'</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'\\x90\\x90\\x90'</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'\\x90\\x90'</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b'\\x90P\\x90P'</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b'x90\\x90\\x90\\x90\\x90\\x90\\x90'</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b'\\x89\\xec]\\xc3'</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b'www'</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b'\\x90'</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b'stdapi_sys_process_getpid'</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b'system'</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b'Username'</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90'</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b'\\x90\\x90\\x85\\xc0\\x85\\xc0\\x90\\x90\\x85\\xc0\\x85...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b'Y]\\xc3'</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90'</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>b'L\\x00i\\x00c\\x00e\\x00n\\x00s\\x00e\\x00d\\x00 \\x0...</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>b'U\\x96KJ^H\\x99\\xf87\\x98N'</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>b'runtime'</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>b',5'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>b'Licensed to: Microsoft.com'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>b'$\\xc0\\xff\\xd9'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>b'XNlWRwdkPH066hL6gWnLczmEyHOPpN8VTyrrb5f71DxS...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                index  stage_append_decoded\n",
       "0                                                                     32162\n",
       "1   b'X5O!P%@AP[4\\\\PZX54(P^)7CC)7}$EICAR-STANDARD-...                  1098\n",
       "2                         b'\\x90\\xd9\\xfa\\x90\\xd9\\xff'                   361\n",
       "3                                     b'\\x90\\x90\\x90'                   250\n",
       "4                                         b'\\x90\\x90'                   230\n",
       "5   b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...                    86\n",
       "6                                       b'\\x90P\\x90P'                    73\n",
       "7                      b'x90\\x90\\x90\\x90\\x90\\x90\\x90'                    62\n",
       "8                                    b'\\x89\\xec]\\xc3'                    40\n",
       "9                                              b'www'                    38\n",
       "10                                            b'\\x90'                    37\n",
       "11                       b'stdapi_sys_process_getpid'                    27\n",
       "12                                          b'system'                    27\n",
       "13                                        b'Username'                    26\n",
       "14                            b'\\x90\\x90\\x90\\x90\\x90'                    26\n",
       "15  b'\\x90\\x90\\x85\\xc0\\x85\\xc0\\x90\\x90\\x85\\xc0\\x85...                    24\n",
       "16                                          b'Y]\\xc3'                    24\n",
       "17                                b'\\x90\\x90\\x90\\x90'                    20\n",
       "18  b'L\\x00i\\x00c\\x00e\\x00n\\x00s\\x00e\\x00d\\x00 \\x0...                    17\n",
       "19                         b'U\\x96KJ^H\\x99\\xf87\\x98N'                    14\n",
       "20                                         b'runtime'                     8\n",
       "21  b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...                     7\n",
       "22                                              b',5'                     4\n",
       "23                        b'\\x90\\x90\\x90\\x90\\x90\\x90'                     4\n",
       "24  b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...                     4\n",
       "25                b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90'                     4\n",
       "26                                               b'1'                     3\n",
       "27                      b'Licensed to: Microsoft.com'                     3\n",
       "28  b'\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90\\x90...                     1\n",
       "29                                   b'$\\xc0\\xff\\xd9'                     1\n",
       "30  b'XNlWRwdkPH066hL6gWnLczmEyHOPpN8VTyrrb5f71DxS...                     1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"stage_append_decoded\"].notna()][\"stage_append_decoded\"].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef807d3-b37c-444d-a3c9-3375646d2ca6",
   "metadata": {},
   "source": [
    "# Export IOCs to CSV\n",
    "\n",
    "Export ip and host indicators to a CSV for easy grep and processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61812065-479b-4623-938a-c8d04fe55510",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hosts_for_bconfig(bconfig):\n",
    "    \"\"\"Return both the configured domains and 'Host:' headers\"\"\"\n",
    "    host_headers = []\n",
    "    for setting in [\"SETTING_C2_REQUEST\", \"SETTING_C2_POSTREQ\"]:\n",
    "        for (key, val) in bconfig.settings.get(setting, []):\n",
    "            if key in (\"_HEADER\", \"_HOSTHEADER\"):\n",
    "                header, _, value = val.partition(b\":\")\n",
    "                if header.lower() == b\"host\":\n",
    "                    host_headers.append(value.strip().decode())\n",
    "    return set(bconfig.domains), set(host_headers)\n",
    "\n",
    "rows = []\n",
    "for index, row in df.iterrows():\n",
    "    bconfig = row[\"bconfig\"]\n",
    "    domains, hostheaders = hosts_for_bconfig(bconfig)\n",
    "    data = [\n",
    "        row.collected_from_ip,\n",
    "        row.collected_from_port,\n",
    "        bconfig.protocol,\n",
    "        \", \".join(sorted(domains)),\n",
    "        \", \".join(sorted(hostheaders)),\n",
    "        row.collected_dt,\n",
    "        row.asn,\n",
    "        row.org,\n",
    "    ]\n",
    "    rows.append(data)\n",
    "\n",
    "export_df = pd.DataFrame(rows, columns=[\"ip\", \"port\", \"protocol\", \"domains\", \"host_header\", \"collected_dt\", \"asn\", \"org\"])\n",
    "export_df[\"asn\"] = export_df[\"asn\"].astype(pd.UInt32Dtype())\n",
    "export_df = export_df.sort_values([\"collected_dt\", \"ip\", \"port\", \"protocol\", \"domains\", \"host_header\"]).reset_index()\n",
    "export_df = export_df.groupby([\"ip\", \"port\", \"protocol\", \"domains\", \"host_header\", \"asn\", \"org\"])[\"collected_dt\"].agg([\"first\", \"last\"]).sort_values([\"first\"])\n",
    "export_df[\"delta_days\"] = (export_df[\"last\"] - export_df[\"first\"]).dt.days\n",
    "export_df = export_df.rename(columns={\"first\": \"first_seen\", \"last\": \"last_seen\"})\n",
    "export_df = export_df.reset_index()[[\"ip\", \"port\", \"protocol\", \"domains\", \"host_header\", \"first_seen\", \"last_seen\", \"delta_days\", \"asn\", \"org\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b4b08a7b-9603-4b5e-9c97-02e20ed0d4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ip</th>\n",
       "      <th>port</th>\n",
       "      <th>protocol</th>\n",
       "      <th>domains</th>\n",
       "      <th>host_header</th>\n",
       "      <th>first_seen</th>\n",
       "      <th>last_seen</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>asn</th>\n",
       "      <th>org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118.24.109.222</td>\n",
       "      <td>443</td>\n",
       "      <td>https</td>\n",
       "      <td>118.24.109.222</td>\n",
       "      <td></td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>282</td>\n",
       "      <td>45090</td>\n",
       "      <td>Shenzhen Tencent Computer Systems Company Limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>185.82.202.251</td>\n",
       "      <td>443</td>\n",
       "      <td>https</td>\n",
       "      <td>185.82.202.214</td>\n",
       "      <td></td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>57</td>\n",
       "      <td>60117</td>\n",
       "      <td>Host Sailor Ltd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>185.82.202.5</td>\n",
       "      <td>443</td>\n",
       "      <td>https</td>\n",
       "      <td>beta.is-an-actor.com</td>\n",
       "      <td>www.amazon.com</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>43</td>\n",
       "      <td>60117</td>\n",
       "      <td>Host Sailor Ltd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>185.82.202.74</td>\n",
       "      <td>443</td>\n",
       "      <td>https</td>\n",
       "      <td>185.183.98.226</td>\n",
       "      <td></td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>9</td>\n",
       "      <td>60117</td>\n",
       "      <td>Host Sailor Ltd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188.166.197.9</td>\n",
       "      <td>443</td>\n",
       "      <td>https</td>\n",
       "      <td>service.serveftp.org</td>\n",
       "      <td></td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>72</td>\n",
       "      <td>14061</td>\n",
       "      <td>DIGITALOCEAN-ASN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ip  port protocol               domains     host_header  \\\n",
       "0  118.24.109.222   443    https        118.24.109.222                   \n",
       "1  185.82.202.251   443    https        185.82.202.214                   \n",
       "2    185.82.202.5   443    https  beta.is-an-actor.com  www.amazon.com   \n",
       "3   185.82.202.74   443    https        185.183.98.226                   \n",
       "4   188.166.197.9   443    https  service.serveftp.org                   \n",
       "\n",
       "  first_seen  last_seen  delta_days    asn  \\\n",
       "0 2018-07-04 2019-04-12         282  45090   \n",
       "1 2018-07-04 2018-08-30          57  60117   \n",
       "2 2018-07-04 2018-08-16          43  60117   \n",
       "3 2018-07-04 2018-07-13           9  60117   \n",
       "4 2018-07-04 2018-09-14          72  14061   \n",
       "\n",
       "                                                 org  \n",
       "0  Shenzhen Tencent Computer Systems Company Limited  \n",
       "1                                    Host Sailor Ltd  \n",
       "2                                    Host Sailor Ltd  \n",
       "3                                    Host Sailor Ltd  \n",
       "4                                   DIGITALOCEAN-ASN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8fbeae0a-0679-456d-a912-b158517e26fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2854680"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(\"iocs-export.csv\", \"w\").write(export_df.to_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8206d62c-819a-4b55-a781-317a0cf9e412",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}