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https://github.com/akafengfeng/AI_Security

This is a paper list about Machine Learning for IDSes
https://github.com/akafengfeng/AI_Security

Last synced: 3 months ago
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This is a paper list about Machine Learning for IDSes

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# AI for Security



## Intrusion Detection Systems

* 2015, BICT,[A Deep Learning Approach for Network Intrusion Detection

System](https://eudl.eu/pdf/10.4108/eai.3-12-2015.2262516)

* 2018, Ph.D. Thesis, [Flow-based Anomaly Detection in High-Speed Networks](https://research-repository.griffith.edu.au/bitstream/handle/10072/367890/Jadidi_2016_01Thesis.pdf?sequence=1)

* 2010, S&P,[Outside the Closed World:On Using Machine Learning For Network Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5504793)

* 2018, NDSS, [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://arxiv.org/pdf/1802.09089.pdf)

* 2018, IEEE Pervasive Computing, [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8490192)

* 2015, ACM CSUR, [Evaluating Computer Intrusion Detection Systems: A Survey of Common Practices](http://delivery.acm.org/10.1145/2810000/2808691/a12-milenkoski.pdf?ip=198.21.198.2&id=2808691&acc=ACTIVE%20SERVICE&key=A79D83B43E50B5B8%2EEB6DCC30042720A5%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1541188429_db4cbaa780f08fdefb0b08e67043a60e)

* 2017, Computer Networks, [Toward a reliable anomaly-based intrusion detection in real-world environments](https://www.sciencedirect.com/science/article/pii/S1389128617303225)

* 2012, Computer Communications, [Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge](https://www.sciencedirect.com/science/article/pii/S0140366412000266)

* 2013, Information Science, [Adversarial Attacks against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues](https://www.sciencedirect.com/science/article/pii/S0020025513002119)

* 2018, IEEE SPW, [Bringing a GAN to a Knife-fight: Adapting Malware Communication to Avoid Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8424635)

* 2017, SISY, [Evaluation of Machine Learning Algorithms for Intrusion Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8080566)

* 2018, arXiv, [Machine Learning DDoS Detection for Consumer Internet of Things Devices](https://arxiv.org/pdf/1804.04159.pdf)

* 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](https://pdfs.semanticscholar.org/1d6e/a73b6e08ed9913d3aad924f7d7ced4477589.pdf)

* 2017, Cluset Computing, [A survey of deep learning-based network anomaly detection](https://link.springer.com/content/pdf/10.1007/s10586-017-1117-8.pdf)

* 2017, ACM SIGCOMM, [Knowledge-Defined Networking](http://delivery.acm.org/10.1145/3140000/3138810/acmdl17-92.pdf?ip=198.21.198.2&id=3138810&acc=ACTIVE%20SERVICE&key=A79D83B43E50B5B8%2EEB6DCC30042720A5%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1541190558_5dee8d31ffbd6636f0fba47f620ebfaf)

* 2017, IEEE Communications Surveys, [State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7932863)

* 2018, IEEE Transactions on Emerging Topics in Computational Intelligence, [A Deep Learning Approach to Network Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8264962)

* 2006, S&P, [A Framework for the Evaluation of Intrusion Detection Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1624001)

* 2018, arXiv, [Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic](https://arxiv.org/pdf/1805.03735.pdf)

* 2017, ACM SIGSAC, [DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning](http://delivery.acm.org/10.1145/3140000/3134015/p1285-du.pdf?ip=198.21.198.2&id=3134015&acc=ACTIVE%20SERVICE&key=A79D83B43E50B5B8%2EEB6DCC30042720A5%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1541191598_39fba667fde10a4b4258ccec31a77f1c)



* 2018, RAID, [Before Toasters Rise Up: A View into the Emerging IoT Threat Landscape](https://link.springer.com/content/pdf/10.1007%2F978-3-030-00470-5_26.pdf)

* 2018, IEEE Access, [Deep Learning-Based Intrusion Detection With Adversaries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8408779)

* 2018, CIKM, [Collaborative Alert Ranking for Anomaly Detection](https://dl.acm.org/citation.cfm?id=3272013)



## Deep Learning



* 2014, AAAI, [Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease.](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8261/8862)

* 2016, KDD, [“Why Should I Trust You?” Explaining the Predictions of Any Classifier](https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf)

* 1999, Biometrics, [Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm](https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.0006-341X.1999.00463.x)

* 2018, KDD, [Adversarial Detection with Model Interpretation](http://people.tamu.edu/~nhliu43/KDD18_adv.pdf)

* 2016, NIPS, [Linear Feature Encoding for Reinforcement Learning](http://papers.nips.cc/paper/6305-linear-feature-encoding-for-reinforcement-learning.pdf)

* 2017, 55th Annual Meeting of the Association for Computational Linguistics, [Visualizing and Understanding Neural Machine Translation](http://www.aclweb.org/anthology/P17-1106)



## Adversarial Examples



* 2014, Nips, [Generative Adversarial Nets](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)

* 2018, arXiv, [Threat of adversarial attacks on deep learning in computer vision: A survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186)

* 2018, Security and Privacy of Machine Learning,  [Adversarial Malware Detection ](https://secml.github.io/class9/)

* 2013, Information Sciences,  [Adversarial Attacks against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues](http://pralab.diee.unica.it/sites/default/files/Corona-INS2013.pdf)  

* 2017, arXiv, [Adversarial Patch](https://arxiv.org/abs/1712.09665)

* 2017, KDD, [Adversary Resistant Deep Neural Networks with an Application to Malware Detection](https://arxiv.org/pdf/1610.01239.pdf)

* 2016, arXiv, [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://arxiv.org/abs/1606.04435)

* 2017, S&P, [Towards Evaluating the Robustness of Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7958570)

* 2017, ACM Workshop on Artificial Intelligence and Security,  [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://dl.acm.org/citation.cfm?id=3140444)

* 2016, S&P, [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7546524)

* 2018, arXiv, [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://arxiv.org/abs/1802.00420)

* 2018, arXiv, [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://arxiv.org/pdf/1801.01944.pdf)

* 2015, arXiv, [Adversarial Autoencoders](https://arxiv.org/pdf/1511.05644.pdf)

* 2016, arXiv, [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://arxiv.org/pdf/1606.04435.pdf)

* 2016, USENIX, [Hidden Voice Commands](https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_carlini.pdf)



## Gernal DNN IDS

* 2009, IEEE CISDA, [A Detailed Analysis of the KDD CPU 99 Data Set](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5356528) 



## RNN IDS

* 2018, CCS, [Tiresias: Predicting Security Events Through Deep Learning](https://seclab.bu.edu/people/gianluca/papers/tiresias-ccs2018.pdf)

* 2018, M.S. Thesis, [Recurrent Neural Network Architectures Toward Intrusion Detection](https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=7745&context=etd)

* 2018, IEEE ICACI, [A network threat analysis method combined with kernel PCA and LSTM-RNN](https://ieeexplore.ieee.org/abstract/document/8377511)

* 2018, IEEE DSC, [An Intelligent Network Attack Detection Method Based on RNN](https://ieeexplore.ieee.org/abstract/document/8411899)

* 2018, IEEE CCECE, [Comparison of Recurrent Neural Network Algorithms for Intrusion Detection Based on Predicting Packet Sequences](https://ieeexplore.ieee.org/abstract/document/8447793)

* 2018, IEEE Communications Magazine, [Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications](https://ieeexplore.ieee.org/abstract/document/8466367)

* 2018, IEEE Access, [An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units](https://ieeexplore.ieee.org/abstract/document/8449272)

* 2018, JISCR, [Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm](https://journals.nauss.edu.sa/index.php/JISCR/article/viewFile/454/462)

* 2018, ICONIP, [A Semantic Parsing Based LSTM Model for Intrusion Detection](https://link.springer.com/chapter/10.1007/978-3-030-04212-7_53)

* 2018, ICCCS, [Comparative Study of CNN and RNN for Deep Learning Based Intrusion Detection System](https://link.springer.com/content/pdf/10.1007%2F978-3-030-00018-9_15.pdf)

* 2018, NetSoft, [Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks](http://eprints.whiterose.ac.uk/129091/1/NetSoft_ShortPaper.pdf)

* 2018, SoutheastCon, [Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection](https://ieeexplore.ieee.org/abstract/document/8478898)

* 2018, ECML PKDD, [Malware Detection by Analysing Encrypted Network Traffic with Neural Networks](https://link.springer.com/content/pdf/10.1007%2F978-3-319-71246-8_5.pdf)

* 2017, IEEE SPW, [Malware Detection by Analysing Network Traffic with Neural Networks](https://www.computer.org/csdl/proceedings/spw/2017/1968/00/1968a205-abs.html)

* 2017, IEEE Access, [A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks](https://ieeexplore.ieee.org/abstract/document/8066291)

* 2017, arXiv, [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://arxiv.org/abs/1803.10769)

* 2017, Researchgate, [Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)](https://www.researchgate.net/publication/324486490_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)](https://etd.ohiolink.edu/!etd.send_file?accession=wright1503680452498351&disposition=inline)

* 2016, PlatCon, [Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection](https://ieeexplore.ieee.org/abstract/document/7456805)

* 2016, FDSE, [Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks](https://link.springer.com/chapter/10.1007/978-3-319-48057-2_9)

* 2015, ICASSP, [Malware classification with recurrent networks](https://ieeexplore.ieee.org/abstract/document/7178304)

* 2015, WISA, [Applying Recurrent Neural Network to Intrusion Detection with Hessian Free Optimization](https://www.researchgate.net/profile/Jihyun_Kim24/publication/309092742_Applying_Recurrent_Neural_Network_to_Intrusion_Detection_with_Hessian_Free_Optimization/links/59ba3f7c458515bb9c4aa1bf/Applying-Recurrent-Neural-Network-to-Intrusion-Detection-with-Hessian-Free-Optimization.pdf)

* 2015, South African Computer Journal, [Applying long short-term memory recurrent neural networks to intrusion detection](http://sacj.cs.uct.ac.za/index.php/sacj/article/viewFile/248/150)

* 2014, ICTACT Journal on Soft Computing, [PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET](http://ictactjournals.in/paper/IJSC_V4_I3_Paper_3-743to752.pdf)

* 2013, SAICSIT, [Evaluating performance of long short-term memory recurrent neural networks on intrusion detection data](https://dl.acm.org/citation.cfm?id=2513490)

* 2012, Neural Computing and Applications, [Intrusion detection using reduced-size RNN based on feature grouping](https://link.springer.com/article/10.1007/s00521-010-0487-0)

* 2012, Computer & Security, [Toward developing a systematic approach to generate benchmark datasets for intrusion detection](https://www.sciencedirect.com/science/article/pii/S0167404811001672)



## Interpretable Models

* 2014, KDD, [Comprehensible Classification Models – a position paper](https://www.kdd.org/exploration_files/V15-01-01-Freitas.pdf)

* 2016, arXiv, [The mythos of model interpretability](https://arxiv.org/pdf/1606.03490.pdf)



## General Interpretation/Explanation \

* 2019, arXiv.[Learning Interpretable Models with Causal Guarantees](https://arxiv.org/pdf/1901.08576.pdf)

* 2018, S&P, [AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf)

* 2018, arXiv, [Interpretable Deep Learning under Fire](https://arxiv.org/pdf/1812.00891.pdf)

* 2018, NIPS, [Explaining Deep Learning Models – A Bayesian Non-parametric Approach](http://papers.nips.cc/paper/7703-explaining-deep-learning-models-a-bayesian-non-parametric-approach.pdf)

* 2018, arXiv, [A Survey Of Methods For Explaining Black Box Models](https://arxiv.org/pdf/1802.01933.pdf)

* 2018, DEFCON Chian, [Scrutinizing the Weakness and Strength of AI System](https://aivillage.org/material/cn18-guo/slides1.pdf)

* 2018, DEFCON USA, [Explanation: Alternative Path to Secure Deep Learning System](http://www.personal.psu.edu/wzg13/talks/defcon_usa_18.pdf) 

* 2018, arXiv, [Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning](https://arxiv.org/pdf/1806.00069.pdf)

* 2018, IEEE Access, [Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8466590)

* 2018, arXiv, [On the Art and Science of Machine Learning Explanations A Discussion with Practical Recommendations and a Use Case](https://arxiv.org/pdf/1810.02909.pdf)

* 2018, arXiv.[Verifiable Reinforcement Learning via Policy Extraction](https://papers.nips.cc/paper/7516-verifiable-reinforcement-learning-via-policy-extraction.pdf)

* 2018, IEEE CIC, [Next Generation Trustworthy Fraud Detection](https://ieeexplore.ieee.org/abstract/document/8537843)

* 2018, IEEE CHI, [Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda](http://www.brianlim.net/wordpress/wp-content/uploads/2018/01/chi2018-intelligibility%20final.pdf)

* 2017, KDD, [Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking](https://dl.acm.org/citation.cfm?id=3098039)

* 2016, KDD, [“Why Should I Trust You?” Explaining the Predictions of Any Classifier](https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf)

* 2013, KDD, [Accurate Intelligible Models with Pairwise Interactions](http://yinlou.github.io/papers/lou-kdd13.pdf)



## Interpretation/Explanation of CNN



* 2019, arXiv, [Interpretable Deep Learning under Fire](https://arxiv.org/pdf/1812.00891.pdf)

* 2019, NIPS, [Explaining Deep Learning Models – A Bayesian Non-parametric Approach](http://www.personal.psu.edu/wzg13/publications/neurips18.pdf)

* 2018, IEEE CVPR, [Interpretable Convolutional Neural Networks](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0490.pdf) 

* 2018, ICCV, [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](http://openaccess.thecvf.com/content_ICCV_2017/papers/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.pdf)

* 2016, CVPR, [Learning Deep Features for Discriminative Localization](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf)

* 2015, ICML, [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](http://proceedings.mlr.press/v37/xuc15.pdf)



## Interpretation/Explanation of RNN/DT



* 2018, arXiv, [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection](https://arxiv.org/pdf/1803.04967.pdf)

* 2018, ACM CCS, [LEMNA: Explaining Deep Learning based Security Applications](http://people.cs.vt.edu/gangwang/ccs18.pdf)

* 2018, NIPS, [Verifiable Reinforcement Learning via Policy Extraction](https://arxiv.org/pdf/1805.08328.pdf)

* 2011, arXiv, [A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning](https://arxiv.org/pdf/1011.0686.pdf)

* 2010, PMLR,   [Efficient Reductions for Imitation Learning](https://ri.cmu.edu/pub_files/2010/5/Ross-AIStats10-paper.pdf)



## Interpretation/Explanation of Recommendation System



* 2019, ACM TOIS, [MMALFM: Explainable Recommendation by Leveraging Reviews and Images](https://arxiv.org/pdf/1811.05318.pdf) (need update)

* 2018, arXiv, [Visually Explainable Recommendation](https://arxiv.org/pdf/1801.10288.pdf)

* 2018, arXiv, [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192.pdf)

* 2016, SIGIR, [Learning to Rank Features for Recommendation over Multiple Categories](http://yongfeng.me/attach/sigir16-chen.pdf)



## Interpretation/Explanation of Networks



* 2018, arXiv, [Demystifying Deep Learning in Networking](https://people.cs.uchicago.edu/~junchenj/docs/DnnVisualizationAPNet_CameraReady.pdf)



## List



* 2018, arXiv, [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697)

* 2018, NIPS, [Explaining Deep Learning Models – A Bayesian Non-parametric Approach](http://www.personal.psu.edu/wzg13/publications/nips18.pdf)

* 2017, NIPS, [A Unified Approach to Interpreting Model Predictions](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf)

* 2018, AAAI, [Anchors: High-Precision Model-Agnostic Explanations](https://pdfs.semanticscholar.org/1c66/90ab404b23d5026dd3ad0c7a49ce2875c1b3.pdf)

* 2018, arXiv, [DÏoT: A Self-learning System for Detecting Compromised IoT Devices](https://arxiv.org/pdf/1804.07474.pdf)

* 2018, arXiv, [IoT-KEEPER: Securing IoT Communications in Edge Networks](https://arxiv.org/pdf/1810.08415.pdf)

* 2017, arXiv, [Deep Reinforcement Learning that Matters](https://arxiv.org/pdf/1709.06560.pdf)

* 2017, arXiv, [Real-time IoT Device Activity Detection in Edge Networks](http://homepage.tudelft.nl/8e79t/files/pre-nss2018.pdf)

* 2018, arXiv, [Peek-a-Boo: I see your smart home activities, even encrypted!](https://arxiv.org/pdf/1808.02741.pdf)

* 2018, arXiv, [A Survey Of Methods For Explaining Black Box Models](https://arxiv.org/pdf/1802.01933.pdf)

* 2018, S&P, [AI^2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8418593&tag=1)

* 2016, NJCCIC, [Hajime: Analysis of a decentralized internet worm for IoT devices](https://security.rapiditynetworks.com/publications/2016-10-16/hajime.pdf)

* 2018, Unknow, [Analyzing the Propagation of IoT Botnets from DNS Leakage](http://www.cs.umd.edu/projects/droot/botnet.pdf)

* 2018, arXiv, [AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet of Things](https://arxiv.org/pdf/1809.10775.pdf)

* 2017, Milcom, [The Mirai Botnet and the IoT Zombie Armies](https://cs.gmu.edu/~astavrou/research/TheMiraiBotnetandtheIoTZombieArmies.pdf)

* 2018, Unknow, [Analyzing the Propagation of IoT Botnets from DNS Leakage](http://www.cs.umd.edu/projects/droot/botnet.pdf)

* 2019, S&P, [DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model](https://www.computer.org/csdl/proceedings/sp/2019/6660/00/666000a381.pdf)

* 2018, WWW, [DRN: A Deep Reinforcement Learning Framework for News Recommendation](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)

* 2018, ICDM, [A Reinforcement Learning Framework for Explainable Recommendation](https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf)

* 2018, arXiv, [Adversarial Training Towards Robust Multimedia Recommender System](https://arxiv.org/pdf/1809.07062.pdf)

* 2018, arXiv, [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192.pdf)

* 2019, S&P,[HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flows](https://www.computer.org/csdl/proceedings/sp/2019/6660/00/666000a430.pdf)

* 2019, arXiv, [Interpretable Deep Learning under Fire](https://arxiv.org/pdf/1812.00891.pdf)

* 2019, Usenix, [Data Mining Approaches for Intrusion Detection](https://www.usenix.org/legacy/publications/library/proceedings/sec98/full_papers/lee/lee.pdf)

* 2019, arXiv,[DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY](https://arxiv.org/pdf/1901.03407.pdf)

* 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](http://www.s3.eurecom.fr/~yanick/publications/2018_oakland_linuxmalware.pdf)



## Botnet

* 2018, USENIX Security, [BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid](https://www.usenix.org/system/files/conference/usenixsecurity18/sec18-soltan.pdf)

* 2017, USENIX Security, [Understanding the Mirai Botnet](https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-antonakakis.pdf?_gclid=5b024b3b7304f5.75584304-5b024b3b730553.60141230&_utm_source=xakep&_utm_campaign=mention135460&_utm_medium=inline&_utm_content=lnk681856874400)

* 2016, Rapidity Networks, [Hajime: Analysis of a decentralized internet worm for IoT devices](http://www.cs.umd.edu/class/fall2017/cmsc818O/papers/hajime-rapidity.pdf)