This is a paper reading list about Machine Learning for IDS
Intrusion Detection Systems
- 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 CSUREvaluating 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, arXiv, Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
- 2018, CCS, Tiresias: Predicting Security Events 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
- 2018, ACM HPDC, Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
- 2018, ACM CCS, LEMNA: Explaining Deep Learning based Security Applications
- 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
Malwares and Attacks
- 2017, USENIX Security, Understanding the Mirai Botnet
- 2018, S&P, Understanding Linux Malware
Botnet
Interpretable Deep Learning
- 2018, ACM CCS, LEMNA: Explaining Deep Learning based Security Applications
- 2016, KDD, “Why Should I Trust You?” Explaining the Predictions of Any Classifier
- 2018, NIPS, Explaining Deep Learning Models – A Bayesian Non-parametric Approach
- 2018, arXiv, A Survey Of Methods For Explaining Black Box Models
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, Unkonw, 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, Unkonw, 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
Others
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