AI for Security

Intrusion Detection Systems

• 2015, BICT,A Deep Learning Approach for Network Intrusion Detection System

• 2018, Ph.D. Thesis, Flow-based Anomaly Detection in High-Speed Networks

• 2010, S&P,Outside the Closed World:On Using Machine Learning For Network Intrusion Detection

• 2018, NDSS, Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

• 2018, IEEE Pervasive Computing, N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

• 2015, ACM CSUR, Evaluating Computer Intrusion Detection Systems: A Survey of Common Practices

• 2017, Computer Networks, Toward a reliable anomaly-based intrusion detection in real-world environments

• 2012, Computer Communications, Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge

• 2013, Information Science, Adversarial Attacks against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues

• 2018, IEEE SPW, Bringing a GAN to a Knife-fight: Adapting Malware Communication to Avoid Detection

• 2017, SISY, Evaluation of Machine Learning Algorithms for Intrusion Detection System

• 2018, arXiv, Machine Learning DDoS Detection for Consumer Internet of Things Devices

• 2005, the third annual conference on privacy, security and trust, Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets

• 2017, Cluset Computing, A survey of deep learning-based network anomaly detection

• 2017, ACM SIGCOMM, Knowledge-Defined Networking

• 2017, IEEE Communications Surveys, State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

• 2018, IEEE Transactions on Emerging Topics in Computational Intelligence, A Deep Learning Approach to Network Intrusion Detection

• 2006, S&P, A Framework for the Evaluation of Intrusion Detection Systems

• 2018, arXiv, Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic

• 2017, ACM SIGSAC, DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

• 2018, RAID, Before Toasters Rise Up: A View into the Emerging IoT Threat Landscape

• 2018, IEEE Access, Deep Learning-Based Intrusion Detection With Adversaries

• 2018, CIKM, Collaborative Alert Ranking for Anomaly Detection

Deep Learning

• 2014, AAAI, Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease.
• 2016, KDD, “Why Should I Trust You?” Explaining the Predictions of Any Classifier
• 1999, Biometrics, Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• 2018, KDD, Adversarial Detection with Model Interpretation
• 2016, NIPS, Linear Feature Encoding for Reinforcement Learning
• 2017, 55th Annual Meeting of the Association for Computational Linguistics, Visualizing and Understanding Neural Machine Translation

Adversarial Examples

• 2014, Nips, Generative Adversarial Nets
• 2018, arXiv, Threat of adversarial attacks on deep learning in computer vision: A survey
• 2018, Security and Privacy of Machine Learning, Adversarial Malware Detection
• 2013, Information Sciences, Adversarial Attacks against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues
• 2017, arXiv, Adversarial Patch
• 2017, KDD, Adversary Resistant Deep Neural Networks with an Application to Malware Detection
• 2016, arXiv, Adversarial Perturbations Against Deep Neural Networks for Malware Classification
• 2017, S&P, Towards Evaluating the Robustness of Neural Networks
• 2017, ACM Workshop on Artificial Intelligence and Security, Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
• 2016, S&P, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
• 2018, arXiv, Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• 2018, arXiv, Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
• 2015, arXiv, Adversarial Autoencoders
• 2016, arXiv, Adversarial Perturbations Against Deep Neural Networks for Malware Classification
• 2016, USENIX, Hidden Voice Commands

Gernal DNN IDS

• 2009, IEEE CISDA, A Detailed Analysis of the KDD CPU 99 Data Set

RNN IDS

• 2018, CCS, Tiresias: Predicting Security Events Through Deep Learning
• 2018, M.S. Thesis, Recurrent Neural Network Architectures Toward Intrusion Detection
• 2018, IEEE ICACI, A network threat analysis method combined with kernel PCA and LSTM-RNN
• 2018, IEEE DSC, An Intelligent Network Attack Detection Method Based on RNN
• 2018, IEEE CCECE, Comparison of Recurrent Neural Network Algorithms for Intrusion Detection Based on Predicting Packet Sequences
• 2018, IEEE Communications Magazine, Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications
• 2018, IEEE Access, An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units
• 2018, JISCR, Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm
• 2018, ICONIP, A Semantic Parsing Based LSTM Model for Intrusion Detection
• 2018, ICCCS, Comparative Study of CNN and RNN for Deep Learning Based Intrusion Detection System
• 2018, NetSoft, Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks
• 2018, SoutheastCon, Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection
• 2018, ECML PKDD, Malware Detection by Analysing Encrypted Network Traffic with Neural Networks
• 2017, IEEE SPW, Malware Detection by Analysing Network Traffic with Neural Networks
• 2017, IEEE Access, A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
• 2017, arXiv, Network Traffic Anomaly Detection Using Recurrent Neural Networks
• 2017, Researchgate, Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)
• 2017, M.S. Thesis, DEEP LEARNING APPROACH FOR INTRUSION DETECTION SYSTEM (IDS) IN THE INTERNET OF THINGS (IOT) NETWORK USING GATED RECURRENT NEURAL NETWORKS (GRU)
• 2016, PlatCon, Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection
• 2016, FDSE, Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks
• 2015, ICASSP, Malware classification with recurrent networks
• 2015, WISA, Applying Recurrent Neural Network to Intrusion Detection with Hessian Free Optimization
• 2015, South African Computer Journal, Applying long short-term memory recurrent neural networks to intrusion detection
• 2014, ICTACT Journal on Soft Computing, PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET
• 2013, SAICSIT, Evaluating performance of long short-term memory recurrent neural networks on intrusion detection data
• 2012, Neural Computing and Applications, Intrusion detection using reduced-size RNN based on feature grouping
• 2012, Computer & Security, Toward developing a systematic approach to generate benchmark datasets for intrusion detection

Interpretable Models

• 2014, KDD, Comprehensible Classification Models – a position paper
• 2016, arXiv, The mythos of model interpretability

General Interpretation/Explanation \

• 2019, arXiv.Learning Interpretable Models with Causal Guarantees
• 2018, S&P, AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
• 2018, arXiv, Interpretable Deep Learning under Fire
• 2018, NIPS, Explaining Deep Learning Models – A Bayesian Non-parametric Approach
• 2018, arXiv, A Survey Of Methods For Explaining Black Box Models
• 2018, DEFCON Chian, Scrutinizing the Weakness and Strength of AI System
• 2018, DEFCON USA, Explanation: Alternative Path to Secure Deep Learning System
• 2018, arXiv, Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
• 2018, IEEE Access, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
• 2018, arXiv, On the Art and Science of Machine Learning Explanations A Discussion with Practical Recommendations and a Use Case
• 2018, arXiv.Verifiable Reinforcement Learning via Policy Extraction
• 2018, IEEE CIC, Next Generation Trustworthy Fraud Detection
• 2018, IEEE CHI, Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
• 2017, KDD, Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
• 2016, KDD, “Why Should I Trust You?” Explaining the Predictions of Any Classifier
• 2013, KDD, Accurate Intelligible Models with Pairwise Interactions

Interpretation/Explanation of CNN

• 2019, arXiv, Interpretable Deep Learning under Fire
• 2019, NIPS, Explaining Deep Learning Models – A Bayesian Non-parametric Approach
• 2018, IEEE CVPR, Interpretable Convolutional Neural Networks
• 2018, ICCV, Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• 2016, CVPR, Learning Deep Features for Discriminative Localization
• 2015, ICML, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Interpretation/Explanation of RNN/DT

• 2018, arXiv, Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
• 2018, ACM CCS, LEMNA: Explaining Deep Learning based Security Applications
• 2018, NIPS, Verifiable Reinforcement Learning via Policy Extraction
• 2011, arXiv, A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
• 2010, PMLR, Efficient Reductions for Imitation Learning

Interpretation/Explanation of Recommendation System

• 2019, ACM TOIS, MMALFM: Explainable Recommendation by Leveraging Reviews and Images (need update)
• 2018, arXiv, Visually Explainable Recommendation
• 2018, arXiv, Explainable Recommendation: A Survey and New Perspectives
• 2016, SIGIR, Learning to Rank Features for Recommendation over Multiple Categories

Interpretation/Explanation of Networks

• 2018, arXiv, Demystifying Deep Learning in Networking

List

• 2018, arXiv, Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
• 2018, NIPS, Explaining Deep Learning Models – A Bayesian Non-parametric Approach
• 2017, NIPS, A Unified Approach to Interpreting Model Predictions
• 2018, AAAI, Anchors: High-Precision Model-Agnostic Explanations
• 2018, arXiv, DÏoT: A Self-learning System for Detecting Compromised IoT Devices
• 2018, arXiv, IoT-KEEPER: Securing IoT Communications in Edge Networks
• 2017, arXiv, Deep Reinforcement Learning that Matters
• 2017, arXiv, Real-time IoT Device Activity Detection in Edge Networks
• 2018, arXiv, Peek-a-Boo: I see your smart home activities, even encrypted!
• 2018, arXiv, A Survey Of Methods For Explaining Black Box Models
• 2018, S&P, AI^2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
• 2016, NJCCIC, Hajime: Analysis of a decentralized internet worm for IoT devices
• 2018, Unknow, Analyzing the Propagation of IoT Botnets from DNS Leakage
• 2018, arXiv, AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet of Things
• 2017, Milcom, The Mirai Botnet and the IoT Zombie Armies
• 2018, Unknow, Analyzing the Propagation of IoT Botnets from DNS Leakage
• 2019, S&P, DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
• 2018, WWW, DRN: A Deep Reinforcement Learning Framework for News Recommendation
• 2018, ICDM, A Reinforcement Learning Framework for Explainable Recommendation
• 2018, arXiv, Adversarial Training Towards Robust Multimedia Recommender System
• 2018, arXiv, Explainable Recommendation: A Survey and New Perspectives
• 2019, S&P,HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flows
• 2019, arXiv, Interpretable Deep Learning under Fire
• 2019, Usenix, Data Mining Approaches for Intrusion Detection
• 2019, arXiv,DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY
• 2020, S&P, [Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation] (https://people.cs.vt.edu/vbimal/publications/syntheticdata-sp20.pdf)

Malwares and Attacks

• 2018, S&P, Understanding Linux Malware

Botnet

• 2018, USENIX Security, BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid
• 2017, USENIX Security, Understanding the Mirai Botnet
• 2016, Rapidity Networks, Hajime: Analysis of a decentralized internet worm for IoT devices

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