notebooks/07_binary_classifier_comparison/binary-classifier-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary Classifier Comparison\n",
    "\n",
    "### Achievement\n",
    "\n",
    "Using the machine learning algorithm of gradient boosted trees in combination with hyperparameter optimization we create a binary classifier, able to distinguish benign from malicious network traffic, with the following performance characteristics:\n",
    "\n",
    "|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\n",
    "|---|---|---|---|---|\n",
    "|0.98266|0.964|0.957|9784|11748|\n",
    "\n",
    "The classifier created in this notebook yields the best performance yet, compared to the estimators trained in previous experiments.\n",
    "\n",
    "\n",
    "## 1. Introduction\n",
    "\n",
    "In this notebook, we will compare different supervised machine learning approaches to create a binary classifier, able to identify malicious network traffic.     \n",
    "\n",
    "In particular, we will compare the following algorithms:\n",
    "* [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) Classifier using [scikit-learn](https://scikit-learn.org/stable/index.html),\n",
    "* [Gradient Boosted Tree](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) Classifier using the [CatBoost](https://catboost.ai/docs/) library and a\n",
    "* Deep Neural Network using [Keras](https://keras.io/) and [Tensorflow 2](https://www.tensorflow.org/).\n",
    "\n",
    "The approaches presented in this notebook were explored in detail in the course of previous experiments and can be examined in the notebooks [ML Prototype](https://github.com/cstub/ml-ids/blob/master/notebooks/03_ml-prototype/ml-prototype.ipynb) and [Deep Learning Classifier](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb). \n",
    "\n",
    "To ensure a fair comparison of the different models, we will conduct hyperparameter search and optimization for the Random Forest and Gradient Boosted Tree algorithms, as the previous experiments used the default parameters of both algorithms for training. We will utilize the [Hyperopt library](https://github.com/hyperopt/hyperopt) to perform hyperparameter search.    \n",
    "The Deep Neural Network presented in this notebook has already been fine-tuned via hyperparameter search in a previous [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb) and will not be retrained, but rather used as created in this experiment.\n",
    "\n",
    "After training and fine-tuning, we will compare the models based on their respective performances on the validation set and select the best performing model. Subsequently, this model will be evaluated on the test set, to obtain an unbiased estimate of the performance of the model.\n",
    "\n",
    "The data used to train and compare the models is taken from the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Dataset Preparation\n",
    "\n",
    "In this section we load the dataset and prepare the data for training. The preprocessed dataset from CIC-IDS-2018 can be found at [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_base_path = r'/home/glados/Development/Projects/ids-2018/processed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc\n",
    "import uuid\n",
    "import joblib as jl\n",
    "import ml_ids.keras.prediction as kp\n",
    "import tensorflow as tf\n",
    "from ml_ids.data.dataset import load_dataset\n",
    "from ml_ids.data.metadata import FEATURES_NO_VARIANCE\n",
    "from ml_ids.visualization import plot_hist, print_binary_performance, plot_pr_curve, plot_pr_curves, plot_pr_threshold_curves\n",
    "from ml_ids.keras.metrics import AveragePrecisionScoreMetric\n",
    "from ml_ids.model_selection import best_precision_for_target_recall\n",
    "from ml_ids.prediction import predict_proba_positive, predict_decision_boundary\n",
    "from ml_ids.tf_utils import enable_gpu_memory_growth\n",
    "from notebook_utils import transform_data, get_best_model_path, print_trial_results\n",
    "from sklearn.metrics import average_precision_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "from tensorflow.keras import models\n",
    "from hyperopt import fmin, hp, tpe, atpe, Trials, STATUS_OK\n",
    "from hyperopt.plotting import main_plot_history, main_plot_vars\n",
    "\n",
    "\n",
    "rand_state = 42\n",
    "tf.random.set_seed(rand_state)\n",
    "np.random.seed(rand_state)\n",
    "\n",
    "enable_gpu_memory_growth()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Data Loading & Preparation\n",
    "\n",
    "In this part we load the dataset, specifing the features to omit. These encompass all features having no variance and features which should not be taken into account, in order to obtain a better generalization of the model.\n",
    "\n",
    "We use `median` imputation as the strategy to impute missing values, as the distributions of all features, for which imputation is necessary, are highly skewed. \n",
    "Futhermore, we scale the data using a `StandardScaler`, as the neural network demands the data to be scaled, and upsample the malicious network traffic to obtain `100000` samples per attack category. We perform upsampling by using the technique of Synthetic Minority Over-sampling provided by the [SMOTE](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html) library.\n",
    "\n",
    "Subsequently, the dataset is splitted into:\n",
    "* training set,\n",
    "* validation set and\n",
    "* test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples:\n",
      "========\n",
      "Training: (12986354, 68)\n",
      "Val:      (1623294, 68)\n",
      "Test:     (1623295, 68)\n",
      "\n",
      "Training labels:\n",
      "================\n",
      "Benign                      10787766\n",
      "DDOS attack-HOIC              548809\n",
      "DDoS attacks-LOIC-HTTP        460953\n",
      "DoS attacks-Hulk              369530\n",
      "Bot                           228953\n",
      "FTP-BruteForce                154688\n",
      "SSH-Bruteforce                150071\n",
      "Infilteration                 129547\n",
      "DoS attacks-SlowHTTPTest      111912\n",
      "DoS attacks-GoldenEye          33206\n",
      "DoS attacks-Slowloris           8792\n",
      "DDOS attack-LOIC-UDP            1384\n",
      "Brute Force -Web                 489\n",
      "Brute Force -XSS                 184\n",
      "SQL Injection                     70\n",
      "Name: label, dtype: int64\n",
      "\n",
      "Validation labels:\n",
      "==================\n",
      "Benign                      1348471\n",
      "DDOS attack-HOIC              68601\n",
      "DDoS attacks-LOIC-HTTP        57619\n",
      "DoS attacks-Hulk              46191\n",
      "Bot                           28619\n",
      "FTP-BruteForce                19336\n",
      "SSH-Bruteforce                18759\n",
      "Infilteration                 16193\n",
      "DoS attacks-SlowHTTPTest      13989\n",
      "DoS attacks-GoldenEye          4151\n",
      "DoS attacks-Slowloris          1099\n",
      "DDOS attack-LOIC-UDP            173\n",
      "Brute Force -Web                 61\n",
      "Brute Force -XSS                 23\n",
      "SQL Injection                     9\n",
      "Name: label, dtype: int64\n",
      "\n",
      "Test labels:\n",
      "============\n",
      "Benign                      1348471\n",
      "DDOS attack-HOIC              68602\n",
      "DDoS attacks-LOIC-HTTP        57619\n",
      "DoS attacks-Hulk              46191\n",
      "Bot                           28619\n",
      "FTP-BruteForce                19336\n",
      "SSH-Bruteforce                18759\n",
      "Infilteration                 16194\n",
      "DoS attacks-SlowHTTPTest      13989\n",
      "DoS attacks-GoldenEye          4151\n",
      "DoS attacks-Slowloris          1099\n",
      "DDOS attack-LOIC-UDP            173\n",
      "Brute Force -Web                 61\n",
      "Brute Force -XSS                 23\n",
      "SQL Injection                     8\n",
      "Name: label, dtype: int64\n",
      "Samples:\n",
      "========\n",
      "Training: (12986354, 68)\n",
      "Val:      (1623294, 68)\n",
      "Test:     (1623295, 68)\n",
      "\n",
      "Missing values:\n",
      "===============\n",
      "Training: 0\n",
      "Val:      0\n",
      "Test:     0\n",
      "\n",
      "Scaling:\n",
      "========\n",
      "Training: min=-2.338481849667957, max=3467.6006434451638\n",
      "Val:      min=-2.338481849667957, max=1919.8280533361315\n",
      "Test:     min=-2.338481849667957, max=2698.612041157329\n",
      "Samples:\n",
      "========\n",
      "Training: (13542229, 68)\n",
      "\n",
      "Training labels:\n",
      "================\n",
      "Counter({0: 10787766, 8: 548809, 10: 460953, 5: 369530, 1: 228953, 11: 154688, 14: 150071, 12: 129547, 6: 111912, 2: 100000, 4: 100000, 7: 100000, 9: 100000, 13: 100000, 3: 100000})\n"
     ]
    }
   ],
   "source": [
    "dataset = load_dataset(dataset_base_path,\n",
    "                       omit_cols=FEATURES_NO_VARIANCE + ['timestamp', 'dst_port', 'protocol'],\n",
    "                       preserve_neg_value_cols=['init_fwd_win_byts', 'init_bwd_win_byts'])\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test, column_names = transform_data(dataset=dataset,\n",
    "                                                                              imputer_strategy='median',\n",
    "                                                                              scaler=StandardScaler,\n",
    "                                                                              attack_samples=100000,\n",
    "                                                                              random_state=rand_state)\n",
    "\n",
    "del dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Target Grouping & Class Weight Calculation\n",
    "\n",
    "In this step we create the binary target variable by grouping all malicious network traffic into a single attack category represented by the positive class `1`, whereas benign traffic is represented as the negative class `0`.\n",
    "\n",
    "Furthermore, we calculate the weights for each class, given by the proportion of samples of this class in the training set.   \n",
    "For the majority class, the class weight is `1`.   \n",
    "For the minority class, the class weight is calculated with the following formula:\n",
    "\\begin{align}\n",
    "\\frac{(sum\\_majority\\_class)}{(sum\\_minority\\_class)} \\\\\n",
    "\\end{align}\n",
    "In our case this translates to:\n",
    "\\begin{align}\n",
    "\\frac{(sum\\_negative\\_class)}{(sum\\_positive\\_class)} \\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_is_attack = (y_train != 0).astype('int')\n",
    "\n",
    "minority_class_weight = len(y_train_is_attack[y_train_is_attack == 0]) / len(y_train_is_attack[y_train_is_attack == 1])\n",
    "\n",
    "class_weights = { \n",
    "    0: 1, \n",
    "    1: minority_class_weight\n",
    "}\n",
    "\n",
    "class_weights_catboost = [1, minority_class_weight]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Random Forest Model\n",
    "\n",
    "### 3.1 Hyperparameter Search-Space Definition and Training\n",
    "\n",
    "In this section of the notebook, we train a [Random Forest Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) using scikit-learn. We aim to find the optimal parameter configuration for the Random Forest algorithm by conducting hyperparameter search via Hyperopt.\n",
    "\n",
    "To use Hyperopt, we have to define the search-spaces for all parameters that should be optimized. The search-spaces are defined as follows:\n",
    "* Number of tree estimators *(n_estimators)*: uniform integer space in the interval of `[10, 100]`.\n",
    "* Information gain criterion *(criterion)*: choice of `gini` or `entropy`.\n",
    "* Maximum depth of a single tree *(max_depth)*: uniform integer space in the interval of `[10, 100]` or `None` resulting in an unbounded tree.\n",
    "* Maximum features for the best split *(max_features)*: choice of `sqrt` or `log2`.\n",
    "* Minimum samples to split a node *(min_samples_split)*: uniform integer space in the interval of `[1, 10]`.\n",
    "* Minimum samples at a leaf node *(min_samples_leaf)*: uniform integer space in the interval of `[2, 10]`.\n",
    "\n",
    "Training is performed on `50` models, using the [Tree of Parzen Estimators (TPE)](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf) search algorithm provided by Hyperopt, to optimize the search process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_random_forest(args):\n",
    "    print('\\nRun') \n",
    "    print('==========')\n",
    "    print('Parameters:\\n{}'.format(args))\n",
    "    \n",
    "    nr_estimators = int(args['nr_estimators'])\n",
    "    criterion = args['criterion']\n",
    "    max_depth = int(args['max_depth']['depth']) if args['max_depth']['depth'] else None\n",
    "    max_features = args['max_features']\n",
    "    min_samples_split = int(args['min_samples_split'])\n",
    "    min_samples_leaf = int(args['min_samples_leaf'])\n",
    "    \n",
    "    model_path = 'models/rf_{}.joblib'.format(uuid.uuid4())\n",
    "    \n",
    "    clf = RandomForestClassifier(n_estimators=nr_estimators,\n",
    "                                 criterion=criterion,\n",
    "                                 max_depth=max_depth,\n",
    "                                 max_features=max_features,\n",
    "                                 min_samples_split=min_samples_split,\n",
    "                                 min_samples_leaf=min_samples_leaf,\n",
    "                                 class_weight='balanced',\n",
    "                                 n_jobs=-1,\n",
    "                                 verbose=1,\n",
    "                                 random_state=rand_state)\n",
    "    \n",
    "    clf.fit(X_train, y_train_is_attack)\n",
    "    \n",
    "    pred = predict_proba_positive(clf, X_val)\n",
    "    pr_score = average_precision_score(y_val.label_is_attack, pred)\n",
    "    \n",
    "    jl.dump(clf, model_path)\n",
    "\n",
    "    print('PR Score: {}'.format(pr_score))\n",
    "    \n",
    "    return {\n",
    "        'loss': -pr_score,\n",
    "        'status': STATUS_OK,\n",
    "        'model_path': model_path\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials_rf = Trials()\n",
    "\n",
    "space = { \n",
    "    'nr_estimators': hp.quniform('nr_estimators', 10, 100, 1),\n",
    "    'criterion': hp.choice('criterion', ['gini', 'entropy']),\n",
    "    'max_depth': hp.choice('max_depth', [\n",
    "        {\n",
    "            'depth': None\n",
    "        },\n",
    "        {\n",
    "            'depth': hp.quniform('nr_max_depth', 10, 100, 1),\n",
    "        }\n",
    "    ]),  \n",
    "    'max_features': hp.choice('max_features', ['sqrt', 'log2']),\n",
    "    'min_samples_split': hp.quniform('min_samples_split', 2, 10, 1),\n",
    "    'min_samples_leaf': hp.quniform('min_samples_leaf', 1, 10, 1)\n",
    "}\n",
    "\n",
    "best_run_rf = fmin(fn=train_random_forest,\n",
    "                   space=space,\n",
    "                   algo=tpe.suggest,\n",
    "                   max_evals=50,\n",
    "                   trials=trials_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Output omitted for brevity]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Hyperparameter Search Results\n",
    "\n",
    "After completion of the hyperparameter search, we plot the parameter search-spaces and highlight the values used for the ten best performing models in each respective search-space.    \n",
    "Evaluating the hyperparameter search results, we can infer the following optimal value-ranges for each parameter:\n",
    "\n",
    "* Number of tree estimators: the optimal number of tree estimators seems to be in the interval of `[60, 100]`.\n",
    "* Information gain criterion: the `entropy` criterion yields a better performance than the `gini` criterion in our search run.\n",
    "* Maximum depth of a single tree: restricting the depth of the trees seems to perform better than using unrestricted trees. A value in the interval of `[20, 50]` for the maximum tree depth yields a good performance.\n",
    "* Maximum features for the best split: the `sqrt` option seems to perform better than `log2`.\n",
    "* Minimum samples to split a node: the optimal number of minimum samples to split a node is in the interval of `[4, 6]`.\n",
    "* Minimum samples of a leaf node: given our search run, there is no conclusive best option for the minimum numbers of samples in a leaf node. However, the values `{3, 6, 7, 8}` seem to perform well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg best error: -0.9810239466346429\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finite loss range -0.9810239466346429 -0.9751055486286375 -0.9805809112883349\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "main_plot_history(trials_rf)\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "main_plot_vars(trials_rf, columns=3, colorize_best=10, arrange_by_loss=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Optimal Model Parameters\n",
    "\n",
    "Looking at the best performing model, we can observe the following parameter configuration:\n",
    "* `80` tree estimators,\n",
    "* the `entropy` information gain criterion,\n",
    "* a maximum depth of `32` nodes per tree,\n",
    "* `sqrt` as the selector for the maximum number of features considered when splitting a node,\n",
    "* a minimum of `3` samples per leaf node and\n",
    "* a minimum of `4` samples necessary to split a node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.9810239466346429\n",
      "Best model path: models/rf_1af6dcf7-4407-469b-89a0-b2b78661b00e.joblib\n",
      "\n",
      "Best model parameters:\n",
      "======================\n",
      "{'criterion': 1, 'max_depth': 1, 'max_features': 0, 'min_samples_leaf': 3.0, 'min_samples_split': 4.0, 'nr_estimators': 80.0, 'nr_max_depth': 32.0}\n"
     ]
    }
   ],
   "source": [
    "print_trial_results(trials_rf, best_run_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Performance\n",
    "\n",
    "The Random Forest model yields a PR score of `0.9810`, with a precision of `0.967` and a recall of `0.955` in respect to the positive class.    \n",
    "The model performs quite well, with the exception of the attack category `Infiltration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf = jl.load(get_best_model_path(trials_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=24)]: Using backend ThreadingBackend with 24 concurrent workers.\n",
      "[Parallel(n_jobs=24)]: Done   2 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=24)]: Done  80 out of  80 | elapsed:    2.9s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average PR Score 0.9810239466346281\n"
     ]
    }
   ],
   "source": [
    "pred_val_proba_rf = predict_proba_positive(clf_rf, X_val)\n",
    "plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=24)]: Using backend ThreadingBackend with 24 concurrent workers.\n",
      "[Parallel(n_jobs=24)]: Done   2 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=24)]: Done  80 out of  80 | elapsed:    2.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "======================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.991     0.993     0.992   1348471\n",
      "           1      0.967     0.955     0.961    274823\n",
      "\n",
      "    accuracy                          0.987   1623294\n",
      "   macro avg      0.979     0.974     0.977   1623294\n",
      "weighted avg      0.987     0.987     0.987   1623294\n",
      "\n",
      "Confusion Matrix:\n",
      "=================\n"
     ]
    },
    {
     "data": {
      "image/png": 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irNHA0aEV3QUYZ2bLzWwFMA7oGpb9Iaybd/sF8j5l51w8tn7ui7qSpie9HmZmw1LkqW9mSwHMbKmkeiG9ETA5ab1FIW1DeJ43PZHnu1BWjqRVQJ3k9Dx56gArzSwnn7IK5EHZOVdaLDOzDtuorPyODlZIeiZ5CiurQN594ZyLhQAp80eGfghdEoTfP4b0RcCuSes1BpaE9Mb5pG+RR1IlYEei7pKCyloG7BTWzVtWgTwoO+diknl/8lZ0e7wEJEZD9AbGJKX3DCMqdic6oTc1dHWslnRg6BM+N0+eRFmnARNCv/NYoLOkWuEEX2dgbFj2dlg37/YL5N0XzrnYZHOYsqRngSOJ+p4XEY2IuAMYJakv8C1wOoCZzZE0CvgMyAEuNbONoaiLiUZyVANeDw+AR4GnJM0naiH3DGUtlzQQmBbWu83MEiccrwdGSPo78FEoo1AelJ1zscnmxSNmdmYBi44uYP1BwKB80qcDrfJJX0cI6vksewx4LJ/0r4mGyaXNg7JzLh5b1zdcbnifsnPOlSDeUnbOxUJAhQreVE7Fg7JzLjbefZGaB2XnXGx8lrjUPCg75+LhJ/rS4kHZOReL6Io+j8qp+OgL55wrQbyl7JyLid+jLx0elJ1zsfGYnJoHZedcbLylnJoHZedcPHz0RVo8KDvnYuGjL9Ljoy+KydABvVg4/namP3djbtotl5zA1JH9mTziBl4ecikNdt4RgA4td2PyiBuYPOIGpoy8gZOP2j83z2md2zF1ZH9mjL6JQVeessU2/nhsW2Y+fxMzRt/EE//ok5u+ZvqDueU9d/+FuekXnXE4s8cMYO1Hg6mz0/ZZ2vOy48H776Nd65a0b9OKc88+k3Xr1vHxrFkcfsiBdGrfhkM6dWDa1KkAjH9rHAd3bE+HNvtxcMf2THx7Qm45M2fMoEOb/WjZohlXX3UF0TS8ZVMxTHJf6mStpSxpI/Ap0QFyI3CZmX2YYVm3Ae+a2VvbsIrF6qmXJzN05Ds8MvDc3LT7nhzPbUNeBeCSM4+gf7/juGLQCOZ8tYRDet3Fxo2b2KVuTaaM7M+r785mxxrV+MdV3Ti4110sW7GGf992Dkd23IuJU+eyZ5Odueb8zvyhz72sXL2WnWvVyN3O2vUbOLDnHb+r06RZX/Pau7N585Ers/8GlHKLFy9myEMP8tEnn1GtWjV6ndmD50aOYOSI/3DTXwfQpetxvPH6a9zU/zreHD+ROnXqMvrFl2nYsCFzZs/mpBO68PXCxQBccdnFDH54GJ0OPJBuJx3Pm2PfoEvX44p5D11xyWZLea2ZtTGz1kB/4PZMCzKzW8pSQAb4YOZXLF/12xZpq39dl/u8erWquS2mtes2sHHjJgCqVqmcm757ozrM+/ZHlq1YA8CEKV/Q7eg2AJzf/WD+NepdVq5eC8BPYZ3CfPzlIr5dmvdmwK4gOTk5rF27Nvr92280aNgQSfzyyy8ArFq1igYNGwLQpm1bGobn+7Zsyfp161i/fj1Lly5l9epfOPCgg5DEWWefy8tjXiy2fcq2YrjzSKkTV59yTWBF4oWka4EeQFXgBTMbIKkp0Qz/7wMHA4uBU8xsraQngFfMbLSk44F7ie5/NRPYw8xOlHQr0ATYI/y+38wejGf3tp1bLz2JXid2ZNWatXTtt7n6B7TajaG3nk2TBrXpe/OTbNy4ia+++4m9m9anSYPaLP5xJScf1ZrKlSoC0Hy36Ka9Ex7/MxUrVODv/3qNcR9+DsB2VSrx/jPXsTFnI/c8Po6XJ34S/46Wco0aNeKqP1/DXns0oVq1ahx9TGeOObYzjRvvykkndKH/9dewadMm3n73918OX/jv87Ru05aqVauyZPFiGjXafEu4Ro0bs2TJ4jh3JVblKLZmLJst5WqSZkn6AngEGAggqTPR/bA6Am2A9pIOD3maAw+ZWUtgJfDH5AIlbQf8CzjOzA4Fds6zzRZAl1D2AEmVs7JnWXTrQy/T/Li/MuL16Vx0xuG56dNmL6T9aYM49Oy7uPb8zlStUomVq9dyxT9G8vSd5zP+sT+zcMnPuS3qihUr0qxJPTpf8ADn9n+Ch285ix1rVANgr+Nv4dBed9H7xie4+9o/snvjusWyr6XZihUreOXlMXw+bwFff7uEX3/7lWefeZph/3qYu+65j/kLvuOue+7j4n59t8j32Zw53Hzj9Qwe8i+AfPuPy2yrUN5STkcc3RctgK7A8HAjws7h8RFRS7cFUTAGWGBms8LzGUDTPGW2AL42swXh9bN5lr9qZuvNbBnRXWvr562UpH6Spkuabjlrt24Ps2jU69NyuyKSfbngB35d+z9aNou+Cr/27mwOP/cejuz9f8z95kfmfxvdrHfxjyt5eeIn5ORsYuGSn5n7zY80axIdw5b+tAqAbxb/zLvT59GmRePfbccVbsL4t2jadHd23nlnKleuTLdupzJ50oc889STdOt+KgB/PO10pk+bmptn0aJFnHF6dx55bDh77LknELWMFy9elLvO4kWLaNCgYbw7E5Niupt1qRPL6AszmwTUJWrZCrg9BOw2ZtbMzBI3E1yflG0jv+9eSfXRpMqPmQ0zsw5m1kGVqhVpP7JtzyabG/4nHLE/c7/5AYDdGtahYsXoo2rSoBZ7Na3PwiU/A+SewNtph2r063EYj78wCYCX3/6YIw7YC4A6O21P893qsWDxz+y0QzWqVK6Um35Qmz34/Ovv49nBMmTXXZswdepkfvvtN8yMtyeMZ+8W+9CgYUPee/cdACa+PYFmzaL2xsqVKzn15BO47e+3c/Ahh+SW06BBA2rU2IEpkydjZvzn6eGcePIp+W6z9CuWu1mXOrH0KUtqAVQEfia6HfdASc+Y2RpJjYANaRb1BbCHpKZm9g1wRlYqHIMnb+/DYe2bU3enGsx/YyADh75G10Nb0ny3emzaZHy7dDlXDBoBwMFt9+Ca8zqzIWcjmzYZV/5jJD+v/BWAe647jf32agTA7cPeyG0pj/vwc445aB9mPn8TGzcaN97/IstX/cqBrXfnnzedySbbRAVV4J7Hx/FFCMqXnHkEV/c+hvp1ajJt1I288f4cLrntP8Xw7pR8HTt1ovupp3FQx3ZUqlSJ1q3b0veCfrRu05Zrr76SnJwcqm63HYMfHgbA0CGD+eqr+dwxaCB3DBoIwMuvv0m9evV4cPDD9PtTH9auXUvnLsf5yItyTtkaE5k0JA6iFu6NZvZqWHYl8KewbA1wNlHL9hUzaxXWuQaoYWa35jnRdxJwN9GJvqlAfTPrFU70rTGze0L+2cCJIXjnq0L1elZ17x7bcK/dtrRi2uDiroIrwCGdOjBjxvQiNV9rNG5h+18xLONtTrr+iBlm1iHjAkqJrLWUzaxiIcseAB7IZ1GrpHXuSXreJ2mdt82sReiffgiYHta5Nc82fneLcOdc8SpP3RCZKo1X9F0gaRYwB9iRaDSGc66k24qTfOUplpe6uS/M7D7gvuKuh3OuaHzui/SUuqDsnCu9PCinVhq7L5xzrszylrJzLjbeUE7Ng7JzLjbefZGaB2XnXDzK2SiKTHlQds7FQn4367R4UHbOxcZjcmo++sI550oQbyk752JTwZvKKXlQds7FxmNyah6UnXOxkHxIXDo8KDvnYlPBY3JKfqLPORebbN55RNKfJc2RNFvSs5K2k1Rb0jhJ88LvWknr95c0X9KXkrokpbeX9GlY9mCYJhhJVSWNDOlTws2eE3l6h23Mk9R7a94jD8rOuVIv3MHoCqBDmEu9ItATuAEYb2bNgfHhNZL2DctbEt1DdIikxBzwDwP9iO4d2jwsB+gLrDCzZkQzVd4ZyqoNDAA6sfmmzbnBv6g8KDvnYpPl+ZQrAdUkVQKqA0uAU4Anw/IngW7h+SnAiHCj5QXAfKCjpAZATTObZNFtmYbnyZMoazRwdGhFdwHGmdlyM1sBjGNzIC8yD8rOuViIcFVfhj9A3cSd6MOjX6JsM1sM3AN8CywFVpnZm0S3i1sa1lkK1AtZGgHfJVVvUUhrFJ7nTd8ij5nlAKuAOoWUlRE/0eeci81WnuhbVtA9+kJ3wSnA7sBK4DlJZxdSVn41sULSM81TZN5Sds7FYytO8qVxou8YYIGZ/WRmG4D/AgcDP4QuCcLvH8P6i4Bdk/I3JuruWBSe503fIk/oItkRWF5IWRnxoOyci00W+5S/BQ6UVD308x4NfA68BCRGQ/QGxoTnLwE9w4iK3YlO6E0NXRyrJR0Yyjk3T55EWacBE0K/81igs6RaocXeOaRlxLsvnHOlnplNkTQamAnkAB8Bw4AawChJfYkC9+lh/TmSRgGfhfUvNbONobiLgSeAasDr4QHwKPCUpPlELeSeoazlkgYC08J6t5nZ8kz3xYOycy4WIrtzX5jZAKKhacnWE7Wa81t/EDAon/TpQKt80tcRgno+yx4DHitilfNVYFCWVLOwjGb2y7aogHOu/PCrrFMrrKU8h9+fWUy8NqBJFuvlnCuDfO6L1AoMyma2a0HLnHOuqIpwEUi5ltboC0k9Jd0YnjeW1D671XLOlUUVpIwf5UXKoCxpMHAUcE5I+g0Yms1KOedceZXO6IuDzaydpI8gd/hHlSzXyzlXBpWf9m7m0gnKGyRVIFw2KKkOsCmrtXLOlUl+oi+1dILyQ8DzwM6S/gb0AP6W1Vo558qcaJxycdei5EsZlM1suKQZRNeWA5xuZrOzWy3nXJmT5mT15V26V/RVBDYQdWH4fBnOuYx4TE4tndEXNwHPAg2JZj/6j6T+2a6Yc86VR+m0lM8G2pvZbwCSBgEzgNuzWTHnXNnj3ReppROUF+ZZrxLwdXaq45wrq/xEX3oKm5DoPqI+5N+AOZLGhtedgffjqZ5zrizxlnJqhbWUEyMs5gCvJqVPzl51nHNlmYfk1AqbkOjROCvinCvbpOzOp1xWpOxTlrQn0UTQ+wLbJdLNbK8s1ss558qldMYcPwE8TvTN4zhgFDAii3VyzpVRWbxHX5mRTlCubmZjAczsKzO7mWjWOOecK5Is3s26zEhnSNz6cFfXryRdBCwG6mW3Ws65sqgcxdaMpROU/0x0R9griPqWdwTOz2alnHNljyhfk9VnKp0JiaaEp6vZPNG9c84VTTnrG85UYRePvECYQzk/ZnZqVmrknHPlWGEt5cGx1aKYtN2nCR9MKfO7WWrNXbq6uKvgCrBuQ2b3uShPJ+wyVdjFI+PjrIhzruzzeX9TS3c+Zeec2yrCW8rp8KDsnIuNzxKXWtpBWVJVM1ufzco458o2D8qppXPnkY6SPgXmhdetJf0z6zVzzrlyKJ1+9weBE4GfAczsY/wya+dcEUVzWPhl1qmk031RwcwW5nlTNmapPs65Msy7L1JLJyh/J6kjYJIqApcDc7NbLedcWVSOGrwZSycoX0zUhdEE+AF4K6Q551zaonv0eVROJZ25L34EesZQF+dcGecXj6SWzp1H/k0+c2CYWb+s1Mg558qxdLov3kp6vh3QHfguO9VxzpVl3nuRWjrdFyOTX0t6ChiXtRo558okyedTTkcmXTy7A7tt64o458q+bN6jT9JOkkZL+kLS55IOklRb0jhJ88LvWknr95c0X9KXkrokpbeX9GlY9mC48xKSqkoaGdKnSGqalKd32MY8Sb235j1K54q+FZKWh8dKolbyjVuzUedc+VRBmT/S8ADwhpm1AFoDnwM3AOPNrDkwPrxG0r5EAxhaAl2BIWHIL8DDQD+geXh0Del9gRVm1gy4D7gzlFUbGAB0AjoCA5KDf5Hfo8IWhiNEa2Dn8KhlZnuY2ahMN+icK58SQ+IyfRRatlQTOBx4FMDM/mdmK4FTgCfDak8C3cLzU4ARZrbezBYA84GOkhoANc1skpkZMDxPnkRZo4GjQ4zsAowzs+VmtoKo4ZoI5EVWaFAOlXrBzDaGR4F3InHOuSyrK2l60iN5BNgewE/A45I+kvSIpO2B+ma2FCD8Ttz0uRFbDlhYFNIahed507fIY2Y5wCqgTiFlZSSd0RdTJbUzs5mZbsQ552CrR18sM7MOBSyrBLQDLjezKZIeIHRVFFSVfNKskPRM8xRZgS1lSYmAfShRYP5S0sxwFPIA7Zwrmq3oT06jT3kRsCjpRs+jiYL0D+vCbj8AABm9SURBVKFLgvD7x6T1d03K3xhYEtIb55O+RZ4QH3cElhdSVkYK676YGn53A/YGjgdOB04Lv51zrki0FT+FMbPviebp2TskHQ18BrwEJEZD9AbGhOcvAT3DiIrdiU7oTQ1dHKslHRj6i8/NkydR1mnAhNClOxboLKlWOMHXOaRlpLDuC4Wd/SrTwp1zLiE60ZfVTVwOPCOpCvA1cB5Rw3OUpL7At4QGpZnNkTSKKHDnAJeaWWL2y4uBJ4BqwOvhAdFJxKckzSdqIfcMZS2XNBCYFta7zcyWZ7oThQXlnSVdXdBCM7s3040658qnbAZlM5sF5NfnfHQB6w8CBuWTPh1olU/6OgroJTCzx4DHilLfghQWlCsCNci/E9s551wWFBaUl5rZbbHVxDlX5pWnO4hkKmWfsnPObQsx9CmXCYUF5Xz7YZxzLiNpzmFR3hUYlLfm7KFzzuXHZ4lLLZ0r+pxzbqt590V6/O4szjlXgnhL2TkXG++9SM2DsnMuJqKCD+pKyYOycy4WwlvK6fCg7JyLR/p3ECnXPCg752LjQ+JS89EXzjlXgnhL2TkXC+9TTo8HZedcbLz7IjUPys652HhMTs2DsnMuFsJPYqXDg7JzLh7y+ZTT4Qcu55wrQbyl7JyLjbeTU/Og7JyLRTR1p4flVDwoO+di4yE5NQ/KzrnYeEM5NQ/KzrmYyEdfpMFHXzjnXAniQbmEufBP59OkYT3at2mVm9b/+mtp3aoFB7Tdnx6ndWflypUATJs6lU7t29CpfRs6tmvNmBdfAOC3336j+8kn0LpVC9q1bsnNN96QW9YD991L2/335YC2+3Nc56NZuHBhvDtYyny/ZBF9zziBbn/oQPejO/LMo0Nyl/3n8aGcfGQ7uh/dkfsG/RWASe9OoOfxh/PHYw+k5/GHM+WDd3LX79vjeE4+sh09uh5Cj66H8POynwD43/r1XHtJH048rDW9Tj6Kxd9t/kwuPqc7h7balcv6nB7THmdP4uKRTB/lRWz7Kqm7JJPUIrxuKumspOVtJB2/FeV/I6nutqhrcTqndx/GvPLGFmlHH3MsM2bNZtpHn9C8+V7cfeftALRs1YoPpkxnyoxZjHn1DS6/5EJycnIAuOrqa/h49hdMnvYRkz78gLFvvA5Am7Zt+WDydKZ99AndTz2Nm/pfF+8OljIVK1bimpsH8eKE6Tw9Zjwjhv+br+Z+wdQP32Xim68xeuwkXhg/lXMvvAKAnWrX4cHHRvL8uMkMvG8oN13Vb4vybn/gEUa98QGj3viAOnV3BuCFkcOpueNOvPLex5z9p0u5//YBuev3ufBK/n7fsPh2OMskZfwoL+I8AJ0JvA/0DK+bAmclLW8DZByUy4pDDzuc2rVrb5F2zLGdqVQp6v7v2OlAFi9aBED16tVz09evW5f7h1u9enWOOPIoAKpUqUKbtu1y8xxx5FFUr179d2W5/O1cfxf22a8NANvX2IE9mu3Nj98v4bmnHuX8S/5MlapVAXID7D6tWlNvlwYANNtrH/63fh3/W7++0G28/earnHzamQAce3w3pn4wETMDoNOhR7J9jRpZ2bfioK14lBexBGVJNYBDgL5sDsp3AIdJmiXpeuA24Izw+gxJHSV9KOmj8HvvUFZFSfdI+lTSJ5Iuz7OtapLekHRBHPsWt+FPPEaXrsflvp46ZQrtWrekQ9v9ePChoblBOmHlypW89urLHPWHo39X1hOPP7pFWa5wi79byBdzPmG/th1YuGA+M6d+SK+Tj+L8049j9sczfrf+W6+NoUXL1rmBG+CWay6hR9dD+NcDd+YG3h+/X8ouDRsDUKlSJWrsUJOVK5bHs1NxkreU0xHX6ItuwBtmNlfSckntgBuAa8zsRABJPwAdzOyy8LomcLiZ5Ug6BvgH8EegH7A70DYsS25W1gBGAMPNbHh+FZHUL5TBrk2aZGNfs+bO2wdRsVIlep7VKzetY6dOzPx4Dl98/jl/Or83Xboex3bbbQdATk4Ovc8+k0suvYLd99hji7KefeZpZs6YzrgJ7+BS++3XNfzlwnO4dsAd1NihJjk5OfyyaiVPj5nA7I9ncO0lfXjt/U9yg8f8Lz/n/ttvYejTL+aW8Y8HH6H+Lg35dc1qrr7wbF55/llOOu2s3OCcrCzGIJ+QKD1xvUdnEgVLwu8z08izI/CcpNnAfUDLkH4MMNTMcgDMLLlJMQZ4vKCAHNYfZmYdzKzDzuErZ2nw9PAnee3VV3hi+DP5thpa7LMP22+/PXNmz85Nu/SifuzZrDmXX3nVFutOGP8Wd94xiNEvvETVpFacy9+GDRu4+sKzOb57D4457mQA6jdoyNHHnYwk9mvTgQoSK5b/DMAPSxfz535n8ff7hrFr080Hw/q7NASibpDju/Xg09C6rt+gId8vibqRcnJyWLP6F3bcacsuLFd+ZD0oS6oD/AF4RNI3wLXAGaTuJhoIvG1mrYCTgO0SRQK/b1pEPgCOUxn7rvPm2Df4v3vuZPQLL+X2BwN8s2BB7om9hQsXMnful+zWtCkAt95yM6t+WcU9996/RVmzPvqIyy65kNH/fYl69erFtg+llZlx67WXskezvTn3gsty04/qfCJTP4y+ZXzz9Tw2bNhArdp1+GXVSi7rczpXXn8rbQ84MHf9nJyc3KC9YcMG3n3rDZrttS8ARx57PC+NfhaAca+9SMeDjyizX9e9+yK1OLovTiPqTrgwkSDpHWATsEPSeqvzvN4RWBye90lKfxO4SNLERPdFUmv5FuCvwBDg4m26FzE59+wzee+diSxbtow9mzbmr7f8jbvvup3169dzYtdjgegE3T+HDOXDD97nnrvvoHKlylSoUIEH/jmEunXrsmjRIu68fRB7t2jBQQe0A+CiSy7jvL5/4sYbruXXNWvo1TMaYrVrkyaMfuGlYtvfku6jaZN55b8jaN6iJT26HgLA5dfdQvczzuGWay/h1GM6UblKFQbeOxRJjHhyGN9+8zXDHryLYQ/eBcDDT79IterVufjs7uTkbGDjxo0ceOiR/PGsPgB0P+NcbrqqHyce1pqaO9XirsGP526/zx+78M1Xc/nt1185tmMLbr17MIcccUzs78O2Un5Ca+aUX3/WNt2ANBG4w8zeSEq7AtgH2AuoCzwBPAmMBSoDtwPfhrSfgAnAOWbWVFIl4C6gK7AB+LeZDQ6t8A7Az8BjwE9mVuh4r/btO9gHU6Zvs31129bcpauLuwquAGeecARzPplZpBjbrGVr+78RYzPeZrf9G8wwsw4ZF1BKZL2lbGZH5pP2YAGrH5Dn9V5Jz/8a8uYAV4dHcplNk16eV9R6OueyKzrR523lVHzuC+dcbMpR13DGfISKc86VIB6UnXMx0Vb9pLWF6OKyjyS9El7XljRO0rzwu1bSuv0lzZf0paQuSentw8Vp8yU9mBjNJamqpJEhfYqkpkl5eodtzJPUe2veJQ/KzrnYSJk/0nQl8HnS6xuA8WbWHBgfXiNpX6Kri1sSDRoYIqliyPMw0QVmzcOja0jvC6wws2ZE107cGcqqDQwAOgEdgQHJwb+oPCg752KRONGX6SNl+VJj4ATgkaTkU4hGcRF+d0tKH2Fm681sATAf6CipAVDTzCZZNDRteJ48ibJGA0eHVnQXYJyZLTezFcA4NgfyIvMTfc65eBStxZuJ+4Hr2PJ6h/pmthTAzJZKSlwx1QiYnLTeopC2ITzPm57I810oK0fSKqBOcno+eYrMW8rOudhsZfdFXUnTkx79NperE4Efzez3M0MVUJV80qyQ9EzzFJm3lJ1zpcWyQi4eOQQ4WdGc7NsBNSU9DfwgqUFoJTcAfgzrLwJ2TcrfGFgS0hvnk56cZ1G4iG1HYHlIPzJPnokZ7SHeUnbOxShboy/MrL+ZNQ4XkfUEJpjZ2cBLQGI0RG+iScsI6T3DiIrdiU7oTQ1dHaslHRj6i8/NkydR1mlhG0Z0JXJnSbXCCb7OIS0j3lJ2zsVCQIX4Lx65AxglqS/R1A2nA5jZHEmjgM+AHOBSM9sY8lxMNPVDNeD18AB4FHhK0nyiFnLPUNZySQOBaWG92/LMXlkkHpSdc7FJd7zx1jCziYTuAzP7Gfj9HR6iZYOAQfmkTwda5ZO+jhDU81n2GNGcO1vNg7JzLjZ+mXVqHpSdc7GJo6Vc2vmJPuecK0G8peyci0UxnegrdTwoO+dikv7EQuWZB2XnXDyyf5l1meBB2TkXG4/JqXlQds7FIupT9rCcio++cM65EsRbys652Hg7OTUPys65+HhUTsmDsnMuNj4kLjUPys652Ph5vtQ8KDvnYuMxOTUffeGccyWIt5Sdc/HxpnJKHpSdc7EQfqIvHR6UnXPx8Lkv0uJB2TkXG4/JqXlQds7Fx6NySj76wjnnShBvKTvnYuKT3KfDg7JzLjZ+oi81D8rOuVgI71JOhwdl51x8PCqn5EHZORcb71NOzUdfOOdcCeItZedcbPxEX2oelJ1zsfGYnJoHZedcPHz4RVo8KDvnYuMn+lLzoOyci4XwPuV0+OgL55wrQbyl7JyLjTeUU/Og7JyLj0fllMp1UJ45c8ayapW1sLjrsQ3VBZYVdyVcgcrS57NbJpn8RF9q5Toom9nOxV2HbUnSdDPrUNz1cPnzz8dP9KXDT/Q552KjrXgUWq60q6S3JX0uaY6kK0N6bUnjJM0Lv2sl5ekvab6kLyV1SUpvL+nTsOxBKTqUSKoqaWRInyKpaVKe3mEb8yT13pr3yIOyc64syAH+Ymb7AAcCl0raF7gBGG9mzYHx4TVhWU+gJdAVGCKpYijrYaAf0Dw8uob0vsAKM2sG3AfcGcqqDQwAOgEdgQHJwb+oPCiXLcOKuwKuUP75ZKmpbGZLzWxmeL4a+BxoBJwCPBlWexLoFp6fAowws/VmtgCYD3SU1ACoaWaTzMyA4XnyJMoaDRwdWtFdgHFmttzMVgDj2BzIi6xc9ymXNWbm//QlWHn/fKLYmv1O5dCt0BaYAtQ3s6UQBW5J9cJqjYDJSdkWhbQN4Xne9ESe70JZOZJWAXWS0/PJU2QelJ1z8dBWn+irK2l60utheQ90kmoAzwNXmdkvKniD+S2wQtIzzVNkHpSdc7HZynbyssJGr0iqTBSQnzGz/4bkHyQ1CK3kBsCPIX0RsGtS9sbAkpDeOJ/05DyLJFUCdgSWh/Qj8+SZWOS9C7xP2TkXnyz1KYe+3UeBz83s3qRFLwGJ0RC9gTFJ6T3DiIrdiU7oTQ1dHaslHRjKPDdPnkRZpwETQr/zWKCzpFrhBF/nkJYRbyk7l0WSFP5xXXYdApwDfCppVki7EbgDGCWpL/AtcDqAmc2RNAr4jGjkxqVmtjHkuxh4AqgGvB4eEAX9pyTNJ2oh9wxlLZc0EJgW1rvNzJZnuiPyv5fSLfFPL6kOYOEPxANBCSPpIGCBmX1f3HUpLvu1aW9jxn2Qcf4961WbUR4uvvHui1IuBOSTgVeAdyR184Bc/CS1lVQlPG8G/J2oRVauSZk/ygvvvijlJLUELgMuAPYEBkqqYmajirdm5d6twHaSTgK+AlYB/wOQVIHoW+rGgrOXPX7jkfR4S7kUk9QQuBrYaGazzWwMUT9af0m9ird25VMIuJjZKcAKYBSwA/ANUD0s2wRUKaYqFq9sXWddhnhLuZSStJuZLZQ0Eegh6VxglJm9Eobr3C5pQmLgvMu+0Je/KTzf2cx6ShoDTAI2Ag0l5RAF5CWS+pvZ2mKscux8lrjUPCiXIkkn9ZoDQyW9ZGYPhKE7BwCbJI02sxclfWBmPxVzlcuVRF++pCuADpIuNrNTJA0F/kA0V0IFoBbwZXkLyC49HpRLkaSTeucBK4FTJVUys/+TtIloAHtFScOBn4uxquWWpG5EY1tPMrNfAczsIknPAQOBbmZWbk/4lacTdpnyoFzChctGN5nZb5J2Am4imsHqc+AgotmwLjWzh8IVTTNDi81HYBSPPYFXwhVklQHMbIOZnS7pUaAh0XjZcsljcmoelEuwEISvAgZLWks4ew+sMbP/SZoJfAL0kbTOzB4trrqWRwWMB18MHC6pppn9EtbrASwys76xV7IkKWdD2zLloy9KMDNbSTTd43ZAdzP7jehSz/+T1DhMUfgx8C5waLhc1MUgOSBLOlXSseECkbFAE+A8SV0lnQncAvgJV8CHX6TmQbmEShpatQQ4GTg79FeOIJpycLykvwD3E12bX41o6JWLQZ6TetcRTVTzONGUkRcA9Yn6lk8Hzgxz9pZrwi8eSYd3X5RAiaFVkuqb2Q9mNkTSL0B3ogPp/USTcu9ENAF3daIJVTK+3t4VnaS2RAfMI4kC84/AI8DVZnZjWKeGma0ptkq6UseDcgkURlkcD9wpaTIw1syeDnPDnkz0ub1kZuvCV+a7gPPNbFHBpbqtFfr465jZV5L2B9YCZxEdGI81s8Mk9QdGSuprZk97QN5SOWrwZsyDcgkkqQNwBtHVei2AwyTVMrN/hzP63YB3gHVErbMzQjeHy5JwQc5ewIlhXt66QK+kUTH/Cav+BNzLlne1cEF56obIlAflEkZSXaJpAz82s3GS3gVOBTpJuiR0ZYw1sx8AzOyrYqxuuRC6k3IkfUl0GftBwHXhxCtE/0ddJO0NHAN0MbPFxVTdEs2v6EvNT/SVMGa2DLiNaNLs081sPfAc8BHQStKu3iqOT5i0PHETzL2I5rB4CGgXJhvCzAYD/yUanniGB+RC+OCLlLylXMySLp0+jOhS6U+IboXeG7hD0iYze17SM0R3zPWAHK/KwCGSbgEws4PCt5mzgJMkrQQqEo0hfzYx94XLXzmKrRnzlnIxCwG5K9F45F+AIcDZZvYa0dV7d0vqYWY5HpDjI2kXADP7EfgB2Bf4IqQtA14GZgHXA08B0zwgu23BW8rFTNKOwInASUS3K/+NaLpHgFeJWmE+j0WMJLUAPpP0ADAVGAp8ChwvaQjRrYMWSBoHjAYqe5dFauVtvHGmPCjHTNKeQGuiOZDHmNkqSd8C9wANgJPDvAndgJ/N7MWQz2/xFJ9fiabb/B74E9EMby8TXa13LNFl75OJTvhdbWbriquipY2f6EvNuy9iJGkvoqvvDgGul3RRWPQVsAtwt5l9G4bE3UnUSgY2X0Hmss/MviNqIbcDugBvE92UcyBRcK4N9AEGe0AuIj/Rl5IH5ZhI2hcYCfQ3s78Q9R1L0t5m9jzRHXO7S3qV6K6515rZxGKrcDkV5qaGqK/YiMYjLwE6EHVhXE406VBvM/usWCpZinlMTs27L+JTG2htZi+H19cR/XNfLOk9M7tUUn2iqR9/NrMvvcsifuHEayIOzCe6EKQd8Odw84AWwA9mtqI461laeZ9yah6UY2Jm70s6QdLXwNfAaDO7TdEdj2dLusHM7iA605/I4wG5GIT3fb2kp4D3gH8m+vbN7ItirZwr8zwox8jMXpd0AdEJo84h7X+S7iSaXMiVIOHbyvXAbpKqJ13B5zIiP9GXBu9TjpmZjSeaVGgugKRmwLVE/ZWu5JkEtC/uSpQFPnVnerylXAzM7DVJmyT9BiwArjKzN4u7Xu73zOwLSWd4K9nFxYNyMTGzN8LcCTXN7I3iro8rmAfkbac8tXgz5UG5GIWuDL8wxJUb3qecmvcplwAekJ1zCd5Sds7Fo5ydsMuUB2XnXCzK25V5mfKg7JyLj0fllLxP2QEgaaOkWZJmS3pOUvWtKOtISa+E5ydLuqGQdXeSdEkG27hV0jXppudZ5wlJpxVhW00lzS5qHd3vaSt+ygsPyi5hrZm1MbNWRHfRuCh5oSJF/nsxs5fC5eMF2QkoclB2pZNfPJKaB2WXn/eAZqGF+HmY2H0msKukzpImSZoZWtQ1ACR1lfSFpPeJbvRKSO8jaXB4Xl/SC5I+Do+DgTuAPUMr/e6w3rWSpkn6RNLfksq6SdKXkt4C9k61E5IuCOV8LOn5PK3/YyS9J2mupBPD+hUl3Z207Qu39o10rqg8KLstSKoEHMfmy773BoabWVuiyd9vBo4xs3bAdOBqSdsB/ya6e8phRHND5+dB4B0za00089oc4Abgq9BKv1ZSZ6A50BFoA7SXdLik9kBPoC1R0D8gjd35r5kdELb3OdA3aVlT4AjgBGBo2Ie+wCozOyCUf4Gk3dPYjkuTT92Zmp/ocwnVJM0Kz98jmtO5IbDQzCaH9AOJ7lX3QZh2uArR3BAtgAVmNg9A0tNAv3y28QfgXAAz2wisUnS36GSdw+Oj8LoGUZDeAXghcXWdpJfS2KdWkv5O1EVSg2giqIRR4Z5688LMfS3CdvdP6m/eMWx7bhrbcukoT9E1Qx6UXcJaM2uTnBAC76/JSUR31D4zz3ptiCaE3xYE3G5m/8qzjasy2MYTQDcz+1hSH+DIpGV5y7Kw7cvNLDl4I6lpEbfrClCeTthlyrsvXFFMBg4JM9shqXq4xdUXwO7h/oMAZxaQfzxwcchbUVJNYDVRKzhhLHB+Ul91I0n1gHeJ7sxSTdIORF0lqewALJVUGeiVZ9npkiqEOu8BfBm2fXFYH0l7Sdo+je24NPgscenxlrJLm5n9FFqcz0qqGpJvNrO5kvoBr0paBrwPtMqniCuBYZL6AhuBi81skqQPwpCz10O/8j7ApNBSXwOcbWYzJY0EZgELibpYUvkrMCWs/ylbBv8vgXeA+sBFZrZO0iNEfc0zFW38J6Bbeu+OS2XmzBljq1VW3a0oYtk2q0wJJp92wTnnSg7vvnDOuRLEg7JzzpUgHpSdc64E8aDsnHMliAdl55wrQTwoO+dcCeJB2TnnShAPys45V4L8PxcOwmvbDkiEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassifications by attack category:\n",
      "======================================\n"
     ]
    },
    {
     "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>misclassified</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_misclassified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Infilteration</th>\n",
       "      <td>12252</td>\n",
       "      <td>16193</td>\n",
       "      <td>0.756623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQL Injection</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -Web</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -XSS</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DoS attacks-Slowloris</th>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>0.007279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benign</th>\n",
       "      <td>8820</td>\n",
       "      <td>1348471</td>\n",
       "      <td>0.006541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bot</th>\n",
       "      <td>17</td>\n",
       "      <td>28619</td>\n",
       "      <td>0.000594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDOS attack-HOIC</th>\n",
       "      <td>22</td>\n",
       "      <td>68601</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDoS attacks-LOIC-HTTP</th>\n",
       "      <td>17</td>\n",
       "      <td>57619</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        misclassified    total  percent_misclassified\n",
       "Infilteration                   12252    16193               0.756623\n",
       "SQL Injection                       1        9               0.111111\n",
       "Brute Force -Web                    4       61               0.065574\n",
       "Brute Force -XSS                    1       23               0.043478\n",
       "DoS attacks-Slowloris               8     1099               0.007279\n",
       "Benign                           8820  1348471               0.006541\n",
       "Bot                                17    28619               0.000594\n",
       "DDOS attack-HOIC                   22    68601               0.000321\n",
       "DDoS attacks-LOIC-HTTP             17    57619               0.000295"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_val_rf = clf_rf.predict(X_val)\n",
    "print_binary_performance(y_val, y_val.label_is_attack, pred_val_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Gradient Boosted Tree Model\n",
    "\n",
    "### 4.1 Hyperparameter Search-Space Definition and Training\n",
    "\n",
    "In this section, we train a [Gradient Boosted Tree Classifier](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) utilizing the [CatBoost](https://catboost.ai/docs/) library.\n",
    "\n",
    "Once again, we perform hyperparameter search to find the optimal parameter configuration for the algorithm, given our trainging data. \n",
    "The parameter search-spaces are defined as follows:\n",
    "* Maximum number of trees *(nr_iterations)*: uniform integer space in the interval of `[100, 2000]`.\n",
    "* Maximum depth of a tree *(depth)*: uniform integer space in the interval of `[4, 10]`.\n",
    "* L2 regularization coefficient *(l2_leaf_reg)*: uniform space in the interval of `[1, 10]`.\n",
    "* Number of splits for numerical features *(border_count)*: a choice of `128` and `254`.\n",
    "* Amount of randomness used for scoring splits *(random_strength)*: uniform integer space in the interval of `[0, 5]`.\n",
    "\n",
    "For a more detailed explanation of the individual parameters consult the CatBoost documentation on [parameter tuning](https://catboost.ai/docs/concepts/parameter-tuning.html).\n",
    "\n",
    "Training is performed on `100` models using the Tree of Parzen Estimators (TPE) search algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pool = Pool(X_train, y_train_is_attack)\n",
    "val_pool = Pool(X_val, y_val.label_is_attack)\n",
    "\n",
    "NR_LOGS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_gradient_boost(args):\n",
    "    print('\\nRun') \n",
    "    print('==========')\n",
    "    print('Parameters:\\n{}'.format(args))\n",
    "    \n",
    "    nr_iterations = int(args['nr_iterations'])\n",
    "    tree_depth = int(args['tree_depth'])\n",
    "    l2_reg = args['l2_reg']\n",
    "    border_count = int(args['border_count'])\n",
    "    random_strength = int(args['random_strength'])\n",
    "    \n",
    "    model_path = 'models/gb_{}.catboost'.format(uuid.uuid4())\n",
    "    \n",
    "    clf = CatBoostClassifier(loss_function='Logloss',                     \n",
    "                             iterations=nr_iterations,\n",
    "                             depth=tree_depth,\n",
    "                             l2_leaf_reg=l2_reg,\n",
    "                             border_count=border_count,\n",
    "                             random_strength=random_strength,\n",
    "                             task_type='GPU',\n",
    "                             class_weights=class_weights_catboost,\n",
    "                             verbose=(nr_iterations // NR_LOGS),\n",
    "                             random_seed=rand_state)\n",
    "\n",
    "    clf.fit(train_pool, eval_set=val_pool)\n",
    "    \n",
    "    pred = predict_proba_positive(clf, val_pool)\n",
    "    pr_score = average_precision_score(y_val.label_is_attack, pred)\n",
    "    \n",
    "    clf.save_model(model_path)\n",
    "    \n",
    "    print('PR Score: {}'.format(pr_score))\n",
    "    \n",
    "    return {\n",
    "        'loss': -pr_score,\n",
    "        'status': STATUS_OK,\n",
    "        'model_path': model_path\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials_gb = Trials()\n",
    "\n",
    "space = { \n",
    "    'nr_iterations': hp.quniform('nr_iterations', 100, 2000, 100),\n",
    "    'tree_depth': hp.quniform('tree_depth', 4, 10, 1),\n",
    "    'l2_reg': hp.uniform('l2_reg', 1, 10),\n",
    "    'border_count': hp.choice('border_count', [128, 254]),\n",
    "    'random_strength': hp.quniform('random_strength', 0, 5, 1)\n",
    "}\n",
    "\n",
    "best_run_gb = fmin(fn=train_gradient_boost,\n",
    "                   space=space,\n",
    "                   algo=tpe.suggest,\n",
    "                   max_evals=100,\n",
    "                   trials=trials_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Output omitted for brevity]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Hyperparameter Search Results\n",
    "\n",
    "After completion of the hyperparameter search, we inspect the results by plotting the parameter values per search-space and highlight the values resulting in the ten best performing models. \n",
    "\n",
    "From the plots we can infer the following optimal value-ranges per parameter:\n",
    "* Maximum number of trees *(nr_iterations)*: a value in the interval of `[1600, 1900]` seems to perform best.\n",
    "* Maximum depth of a tree *(depth)*: the optimal value for the maximum dept of trees is `10` in all best performing cases. This suggests that another round of hyperparameter search with a higher value might yield a better result.\n",
    "* L2 regularization coefficient *(l2_leaf_reg)*: a value in the interval of `[2, 6]` yields good results.\n",
    "* Number of splits for numerical features *(border_count)*: a border count of `254` was chosen for all of the best models.\n",
    "* Amount of randomness used for scoring splits *(random_strength)*: the optimal value seems to be in the inteval of `[3, 5]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg best error: -0.9826628201037251\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finite loss range -0.9826628201037251 -0.9720802673898308 -0.9825280984036184\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "main_plot_history(trials_gb)\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "main_plot_vars(trials_gb, columns=3, colorize_best=10, arrange_by_loss=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Optimal Model Parameters\n",
    "\n",
    "The optimal parameter configuration obtained via the best performing model is as follows:\n",
    "* usage of `1900` trees, \n",
    "* a maximum depth of `10` per tree,\n",
    "* a L2 regularization coefficient of `4.8139`,\n",
    "* a border count of `254` and\n",
    "* a random strenght parameter of `5`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.9826628201037251\n",
      "Best model path: models/gb_835066e8-2427-48ca-a521-67195008cb91.catboost\n",
      "\n",
      "Best model parameters:\n",
      "======================\n",
      "{'border_count': 1, 'l2_reg': 4.813919374945952, 'nr_iterations': 1900.0, 'random_strength': 5.0, 'tree_depth': 10.0}\n"
     ]
    }
   ],
   "source": [
    "print_trial_results(trials_gb, best_run_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Performance\n",
    "\n",
    "We obtain a model with a PR score of `0.9826`, a precision of `0.964` and a recall of `0.957` for the positive class.    \n",
    "Looking at the confusion matrix and misclassification report, we observe a generally well performing model with the exception of the `Infiltration` attack category, which has a rather high misclassification rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f27b94f0518>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gb = CatBoostClassifier()\n",
    "clf_gb.load_model(get_best_model_path(trials_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average PR Score 0.9826628201037251\n"
     ]
    }
   ],
   "source": [
    "pred_val_proba_gb = predict_proba_positive(clf_gb, val_pool)\n",
    "plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "======================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.991     0.993     0.992   1348471\n",
      "           1      0.964     0.957     0.961    274823\n",
      "\n",
      "    accuracy                          0.987   1623294\n",
      "   macro avg      0.978     0.975     0.976   1623294\n",
      "weighted avg      0.987     0.987     0.987   1623294\n",
      "\n",
      "Confusion Matrix:\n",
      "=================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassifications by attack category:\n",
      "======================================\n"
     ]
    },
    {
     "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>misclassified</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_misclassified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Infilteration</th>\n",
       "      <td>11690</td>\n",
       "      <td>16193</td>\n",
       "      <td>0.721917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQL Injection</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -XSS</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -Web</th>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>0.032787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benign</th>\n",
       "      <td>9784</td>\n",
       "      <td>1348471</td>\n",
       "      <td>0.007256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DoS attacks-Slowloris</th>\n",
       "      <td>6</td>\n",
       "      <td>1099</td>\n",
       "      <td>0.005460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bot</th>\n",
       "      <td>35</td>\n",
       "      <td>28619</td>\n",
       "      <td>0.001223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDoS attacks-LOIC-HTTP</th>\n",
       "      <td>13</td>\n",
       "      <td>57619</td>\n",
       "      <td>0.000226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        misclassified    total  percent_misclassified\n",
       "Infilteration                   11690    16193               0.721917\n",
       "SQL Injection                       1        9               0.111111\n",
       "Brute Force -XSS                    1       23               0.043478\n",
       "Brute Force -Web                    2       61               0.032787\n",
       "Benign                           9784  1348471               0.007256\n",
       "DoS attacks-Slowloris               6     1099               0.005460\n",
       "Bot                                35    28619               0.001223\n",
       "DDoS attacks-LOIC-HTTP             13    57619               0.000226"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_val_gb = clf_gb.predict(X_val)\n",
    "print_binary_performance(y_val, y_val.label_is_attack, pred_val_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Deep Neural Network Model\n",
    "\n",
    "In this section, we load the Deep Neural Network model trained in a previous [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb), to compare the models performance on the validation set with the performances of the other models trained in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_dl = models.load_model('../06_dl_classifier/models/opt_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average PR Score 0.9781612882599011\n"
     ]
    }
   ],
   "source": [
    "pred_val_proba_dl = kp.predict_proba(clf_dl, X_val)\n",
    "plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "======================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.991     0.993     0.992   1348471\n",
      "           1      0.967     0.954     0.960    274823\n",
      "\n",
      "    accuracy                          0.987   1623294\n",
      "   macro avg      0.979     0.974     0.976   1623294\n",
      "weighted avg      0.987     0.987     0.987   1623294\n",
      "\n",
      "Confusion Matrix:\n",
      "=================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassifications by attack category:\n",
      "======================================\n"
     ]
    },
    {
     "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>misclassified</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_misclassified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Infilteration</th>\n",
       "      <td>12466</td>\n",
       "      <td>16193</td>\n",
       "      <td>0.769839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQL Injection</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -Web</th>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>0.049180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -XSS</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benign</th>\n",
       "      <td>8966</td>\n",
       "      <td>1348471</td>\n",
       "      <td>0.006649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DoS attacks-Slowloris</th>\n",
       "      <td>6</td>\n",
       "      <td>1099</td>\n",
       "      <td>0.005460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bot</th>\n",
       "      <td>104</td>\n",
       "      <td>28619</td>\n",
       "      <td>0.003634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDoS attacks-LOIC-HTTP</th>\n",
       "      <td>45</td>\n",
       "      <td>57619</td>\n",
       "      <td>0.000781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH-Bruteforce</th>\n",
       "      <td>2</td>\n",
       "      <td>18759</td>\n",
       "      <td>0.000107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        misclassified    total  percent_misclassified\n",
       "Infilteration                   12466    16193               0.769839\n",
       "SQL Injection                       2        9               0.222222\n",
       "Brute Force -Web                    3       61               0.049180\n",
       "Brute Force -XSS                    1       23               0.043478\n",
       "Benign                           8966  1348471               0.006649\n",
       "DoS attacks-Slowloris               6     1099               0.005460\n",
       "Bot                               104    28619               0.003634\n",
       "DDoS attacks-LOIC-HTTP             45    57619               0.000781\n",
       "SSH-Bruteforce                      2    18759               0.000107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_val_dl = kp.predict(clf_dl, X_val)\n",
    "print_binary_performance(y_val, y_val.label_is_attack, pred_val_dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Model Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot illustrates the Precision / Recall curves of the different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pr_curves(y_val.label_is_attack, \n",
    "               {\n",
    "                   'Random Forest': pred_val_proba_rf,\n",
    "                   'Gradient Boost': pred_val_proba_gb,\n",
    "                   'Neural Network': pred_val_proba_dl\n",
    "               }, \n",
    "               size=(20, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the results, we observe similar performance metrics across the different models.    \n",
    "\n",
    "Both the Random Forest and Gradient Boosted Tree models perform better than the Neural Network though, achieving a slighty higher PR score, precision and recall.\n",
    "\n",
    "Overall, the Gradient Boosted Tree model yields the best performance, having the highest PR score and recall in respect to the positive class, with only a slightly lower precision compared to the Random Forest model. Consequently, this model also returns the lowest amount of false-negatives. \n",
    "\n",
    "|Model|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\n",
    "|---|---|---|---|---|---|\n",
    "|Random Forest         |0.98102|**0.967**|0.955|**8820**|12322|\n",
    "|Gradient Boosted Trees|**0.98266**|0.964|**0.957**|9784|**11748**|\n",
    "|Deep Neural Network   |0.97816|**0.967**|0.954|8966|12629|\n",
    "\n",
    "We choose to use the Gradient Bosted Tree model going forward, as it achieves the best overall performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Precision / Recall Decision Boundary\n",
    "\n",
    "All models presented in this notebook use class probabilities (in the interval `[0, 1]`) to predict the class of a given sample. In the case of binary classification a probability threshold for the positive class is chosen, that when exceeded, assigns the sample to the positive class.    \n",
    "This probability threshold is called the *decision boundary* and is usually defined at the value of `0.5`.\n",
    "\n",
    "Changing the decision boundary allows us to modify the metrics for precision and recall, as a decreased decision boundary leads to a lower precision but higher recall, whereas an increased decision boundary has the opposite result, a higher precision and lower recall. This is called the precision-recall tradeoff.\n",
    "\n",
    "The following graphic illustrates this tradeoff by plotting the precision and recall for different values of the decision boundary. We can observe that an increase of precision or recall close to `1.00` leads to a drastic decrease in the opposite metric, so we have to be careful while tuning either one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pr_threshold_curves(y_val.label_is_attack, pred_val_proba_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the graphic depiction might be hard to interpret precisely, we now print the performance metrics for different choices of the decision boundary.    \n",
    "The decision boundaries are chosen for a range of different target recalls ranging from `0.96 - 1.00` in steps of `0.01`. A specific decision boundary is determined by choosing a threshold from the precision-recall curve having the best precision while still satisfying the recall objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Recall of 0.96\n",
      "=====================\n",
      "Decision Boundary: 0.3556530142617532\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.992     0.990     0.991   1348471\n",
      "           1      0.952     0.960     0.956    274823\n",
      "\n",
      "    accuracy                          0.985   1623294\n",
      "   macro avg      0.972     0.975     0.974   1623294\n",
      "weighted avg      0.985     0.985     0.985   1623294\n",
      "\n",
      "\n",
      "Target Recall of 0.97\n",
      "=====================\n",
      "Decision Boundary: 0.07857565826548527\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.994     0.942     0.967   1348471\n",
      "           1      0.774     0.970     0.861    274823\n",
      "\n",
      "    accuracy                          0.947   1623294\n",
      "   macro avg      0.884     0.956     0.914   1623294\n",
      "weighted avg      0.956     0.947     0.949   1623294\n",
      "\n",
      "\n",
      "Target Recall of 0.98\n",
      "=====================\n",
      "Decision Boundary: 0.05394934038983959\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.995     0.832     0.906   1348471\n",
      "           1      0.543     0.980     0.699    274823\n",
      "\n",
      "    accuracy                          0.857   1623294\n",
      "   macro avg      0.769     0.906     0.803   1623294\n",
      "weighted avg      0.919     0.857     0.871   1623294\n",
      "\n",
      "\n",
      "Target Recall of 0.99\n",
      "=====================\n",
      "Decision Boundary: 0.04206439940655072\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.997     0.671     0.802   1348471\n",
      "           1      0.380     0.990     0.549    274823\n",
      "\n",
      "    accuracy                          0.725   1623294\n",
      "   macro avg      0.688     0.830     0.675   1623294\n",
      "weighted avg      0.892     0.725     0.759   1623294\n",
      "\n",
      "\n",
      "Target Recall of 1.0\n",
      "=====================\n",
      "Decision Boundary: 0.0007288180142539065\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      1.000     0.189     0.319   1348471\n",
      "           1      0.201     1.000     0.335    274823\n",
      "\n",
      "    accuracy                          0.327   1623294\n",
      "   macro avg      0.600     0.595     0.327   1623294\n",
      "weighted avg      0.865     0.327     0.321   1623294\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "recalls = np.arange(0.96, 1, 0.01)\n",
    "\n",
    "pred_val_proba_gb = predict_proba_positive(clf_gb, X_val)\n",
    "\n",
    "for recall in recalls:\n",
    "    b = best_precision_for_target_recall(y_val.label_is_attack, pred_val_proba_gb, target_recall=recall)\n",
    "    pred_val_gb = predict_decision_boundary(clf_gb, X_val, b)\n",
    "    print('Target Recall of {}'.format(recall))\n",
    "    print('=====================')\n",
    "    print('Decision Boundary: {}\\n'.format(b))\n",
    "    print(classification_report(y_val.label_is_attack, pred_val_gb, digits=3))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the different decision boundaries, we obtain the following performance metrics:\n",
    "\n",
    "|Decision Boundary|Precision Positive|Recall Positive|F1 (macro)|\n",
    "|---|---|---|---|\n",
    "|0.5 (default)|0.964|0.957|0.976|\n",
    "|0.3556       |0.952|0.960|0.974|\n",
    "|0.0786       |0.774|0.970|0.914|\n",
    "|0.0539       |0.543|0.980|0.803|\n",
    "|0.0420       |0.380|0.990|0.675|\n",
    "|0.0007       |0.201|1.000|0.327|\n",
    "\n",
    "Increasing the recall to a value higher than `0.96`, we observe a drastic decrease in precision, as the number of false-positives increases dramatically.     \n",
    "Given this insight and the fact that the default decision boundary at `0.5` already leads to a recall of approximately `0.96`, changing the decision boundary does not seem worthwhile, as the negative performance impact for further increasing the recall is to severe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Performance on Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we use our best performing model to make predictions on the test set in order to obtain an unbiased performance estimate of the model.\n",
    "\n",
    "The results demonstrate that the performance on the test set is very similar to the performance on the validation set, suggesting that we have built a model that generalizes well to unseen data, provided that the data has similar statistical characteristics as the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "======================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.991     0.993     0.992   1348471\n",
      "           1      0.964     0.957     0.961    274824\n",
      "\n",
      "    accuracy                          0.987   1623295\n",
      "   macro avg      0.978     0.975     0.976   1623295\n",
      "weighted avg      0.987     0.987     0.987   1623295\n",
      "\n",
      "Confusion Matrix:\n",
      "=================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassifications by attack category:\n",
      "======================================\n"
     ]
    },
    {
     "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>misclassified</th>\n",
       "      <th>total</th>\n",
       "      <th>percent_misclassified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Infilteration</th>\n",
       "      <td>11811</td>\n",
       "      <td>16194</td>\n",
       "      <td>0.729344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -Web</th>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>0.049180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brute Force -XSS</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DoS attacks-Slowloris</th>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>0.007279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benign</th>\n",
       "      <td>9690</td>\n",
       "      <td>1348471</td>\n",
       "      <td>0.007186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bot</th>\n",
       "      <td>24</td>\n",
       "      <td>28619</td>\n",
       "      <td>0.000839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDoS attacks-LOIC-HTTP</th>\n",
       "      <td>9</td>\n",
       "      <td>57619</td>\n",
       "      <td>0.000156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        misclassified    total  percent_misclassified\n",
       "Infilteration                   11811    16194               0.729344\n",
       "Brute Force -Web                    3       61               0.049180\n",
       "Brute Force -XSS                    1       23               0.043478\n",
       "DoS attacks-Slowloris               8     1099               0.007279\n",
       "Benign                           9690  1348471               0.007186\n",
       "Bot                                24    28619               0.000839\n",
       "DDoS attacks-LOIC-HTTP              9    57619               0.000156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_test_gb = clf_gb.predict(X_test)\n",
    "print_binary_performance(y_test, y_test.label_is_attack, pred_test_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we used different machine learning algorithms in combination with hyperparameter optimization to train a variety of models and compare their respective performances.    \n",
    "Using the method of Gradient Boosted Trees we obtained the best performing binary classifier, achieving a very compelling performance on the validation and test sets, only falling short in correctly classifying malicious network traffic of the type `Infiltration`.    \n",
    "As concluded in an earlier [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb), other approaches of reliably detecting infiltration attacks have to be explored in the future."
   ]
  }
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