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Machine Learning For Cybersecurity.
https://github.com/opensci-hub/Awesome-ML-Cybersecurity

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Machine Learning For Cybersecurity.

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![Awesome-ML-Cybersecurity](ml_cybersecurity.png)

# Awesome Machine Learning And Cybersecurity [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



**A curated list of Machine Learning and Cybersecurity from multiple sources to help make your studies easier.**

Contributions welcome! Read the [contribution guidelines](contributing.md) first.



## Contents

- [Recommendations](#Recommendations)

- [Dataset Repositories](#dataset-repositories)

- [Scholars Domain](#scholars-domain)

- [Books](#Books)

- [Talks](#Talks)

- [Tutorials](#Tutorials)

- [Open Source Project](#open-source-project)

- [Links Domain](#links-domain)

- [References for reseacher](#references-for-reseacher)

- [Deep Learning Papers](#deep-learning-papers)

- [Deep Reinforcement Learning Papers](#deep-reinforcement-learning-papers)

- [Artificial Intelligence Resources on the Internet 2019](AIResources.pdf)

- [CC0 License](#License)

---



## [↑](#Contents)Recommendations

- [Malthusian Reinforcement Learning](https://arxiv.org/abs/1812.07019), J.Z. Leibo et al., arXiv, 2018. 

- [Learning Unsupervised Learning Rules](https://arxiv.org/abs/1804.00222), L. Metz et al., arXiv, 2018.

- [GAN Dissection: Visualizing and Understanding Generative Adversarial Networks](https://arxiv.org/abs/1811.10597), D. Bau et al., arXiv, 2018.

- [Neural Ordinary Differential Equations](https://arxiv.org/abs/1806.07366), R.T.Q. Chen et al., arXiv, 2018.



## [↑](#Contents)Dataset Repositories

| Repository | Description |

|:----:|:----:|

| [Samples of Security Related Data](http://www.secrepo.com/) | samples of various types of Security related |

| [DARPA Intrusion Detection Evaluation](https://www.ll.mit.edu/r-d/datasets/1999-darpa-intrusion-detection-evaluation-dataset) | Intrusion detection systems were tested in the off-line evaluation using network traffic and audit logs collected on a simulation network |

| [Stratosphere IPS](https://stratosphereips.org/category/dataset.html)| The Stratosphere IPS feeds itself with models created from real malware traffic captures.  |

| [Syber Security Datasets](https://csr.lanl.gov/data/) | Open dataset |

| [Data Capture from National Security Agency](http://www.westpoint.edu/crc/SitePages/DataSets.aspx) | The National Security Agency permitted both the recording and release of the following datasets.  |

| [The ADFA Intrusion Detection Datasets](https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-IDS-Datasets/) | ADFA IDS Datasets which cover both Linux and Windows |

| [NSL-KDD Dataset](https://github.com/defcom17/NSL_KDD) | NSL-KDD dataset |

| [Detecting Malicious URLs](http://sysnet.ucsd.edu/projects/url/) | The data set consists of about 2.4 million URLs (examples) and 3.2 million features. |

| [Multi-Source Cyber-Security Events](http://csr.lanl.gov/data/cyber1/) | This data set represents 58 consecutive days of de-identified event data collected from five sources within Los Alamos National Laboratory’s corporate, internal computer network. |

| [Malware Training Sets](http://marcoramilli.blogspot.cz/2016/12/malware-training-sets-machine-learning.html) | A machine learning dataset for everyone|

| [KDD Cup 1999 Data](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html) |This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. |

| [Web attack payloads](https://github.com/foospidy/payloads) | A collection of web attack payloads. |

| [WAF Malicious request data set](https://github.com/faizann24/Fwaf-Machine-Learning-driven-Web-Application-Firewall) | Machine learning driven web application firewall to detect malicious queries with high accuracy.  |

| [Malware training data set](https://github.com/marcoramilli/MalwareTrainingSets) |Free Malware Training Datasets for Machine Learning |

| [DeepEnd Criminal data set in research](https://www.dropbox.com/sh/7fo4efxhpenexqp/AADHnRKtL6qdzCdRlPmJpS8Aa/CRIME?dl=0) | Criminal dataset in research|

| [Publicly available PCAP files](http://www.netresec.com/?page=PcapFiles) | This is a list of public packet capture repositories, which are freely available on the Internet. |

| [Masquerading User Data](http://www.schonlau.net/)| A data set with seeded masquerading users to compare various intrusion detection methods. |



## [↑](#Contents)Scholars Domain

| Website | Description |

|:----:|:----:|

| [arXiv](https://arxiv.org/) | Open access to 1,486,985 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, Statistics, Electrical Engineering and Systems Science, and Economics. |

| [Search Studies](https://www.icpsr.umich.edu) | ICPSR advances and expands social and behavioral research, acting as a global leader in data stewardship and providing rich data resources and responsive educational opportunities for present and future generations.|

| [Nature Research](https://www.nature.com/) | Is the world’s leading multidisciplinary science journal |

| [Springer Link](https://link.springer.com/) | Providing researchers with access to millions of scientific documents from journals, books, series, protocols and reference works. |

| [National Center for Biotechnology Information](https://www.ncbi.nlm.nih.gov/pmc/) | A free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine.  |

| [EBSCO Host](http://connection.ebscohost.com/) | An intuitive online research platform used by thousands of institutions and millions of users worldwide. With quality databases and search features, EBSCOhost helps researchers of all kinds find the information they need fast. |

| [IEEE Xplore Digital Library](https://ieeexplore.ieee.org/Xplore/home.jsp) | Delivering full text access to the world's highest quality technical literature in engineering and technology. |

| [Wiley Online Library](https://onlinelibrary.wiley.com/) | Hosting one of the world's most extensive multidisciplinary collections of online resources covering life, health and physical sciences, social science, and the humanities. |

| [Cogprints](http://cogprints.org/index.html) | An electronic archive for self-archive papers in any area of Psychology, Neuroscience, and Linguistics, and many areas of Computer Science. |

| [ScienceDirect](https://www.sciencedirect.com/) |An large collection of Physical Sciences and Engineering publications, covering a range of disciplines, from the theoretical to the applied. |

| [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)| Maintain 463 data sets as a service to the machine learning community.|

| [Scopus](https://www.scopus.com/) | The largest abstract and citation database of peer-reviewed literature: Scientific journals...|



## [↑](#Contents)Books

- [Artificial Intelligence In The 21st Century](https://www.amazon.com/Artificial-Intelligence-Century-Stephen-Lucci/dp/1683922239)

- [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)

- [Data Mining and Machine Learning in Cybersecurity](https://www.amazon.com/Data-Mining-Machine-Learning-Cybersecurity/dp/1439839425)

- [Machine Learning and Data Mining for Computer Security](https://www.amazon.com/Machine-Learning-Mining-Computer-Security/dp/184628029X)

- [Network Anomaly Detection](https://www.amazon.com/Network-Anomaly-Detection-Learning-Perspective/dp/1466582081)

- [Deep Learning For Web Security](https://www.amazon.cn/dp/B0788XQ5SN)

- [Reinforcement Learning And GAN For Web Security](https://www.amazon.cn/dp/B07C9Y19BS)

- [Multi-Agent Machine Learning: A Reinforcement Approach](https://www.amazon.com/Multi-Agent-Machine-Learning-Reinforcement-Approach/dp/111836208X)

- [Reinforcement Learning: State-of-the-Art](https://www.amazon.com/Reinforcement-Learning-State-Art-Optimization/dp/364227644X)



## [↑](#Contents)Talks

- [Using Machine Learning to Support Information Security](https://www.youtube.com/watch?v=tukidI5vuBs)

- [Defending Networks with Incomplete Information](https://www.youtube.com/watch?v=36IT9VgGr0g)

- [Applying Machine Learning to Network Security Monitoring](https://www.youtube.com/watch?v=vy-jpFpm1AU)

- [Measuring the IQ of your Threat Intelligence Feeds ](https://www.youtube.com/watch?v=yG6QlHOAWiE)

- [Metrics On Indicator Dissemination And Sharing](https://www.youtube.com/watch?v=6JMEKnes-w0)

- [Applied Machine Learning for Data Exfil and Other Fun Topics](https://www.youtube.com/watch?v=dGwH7m4N8DE)

- [Secure Because Math: A Deep-Dive on ML-Based Monitoring](https://www.youtube.com/watch?v=TYVCVzEJhhQ)

- [Pwning Deep Learning Systems](https://www.youtube.com/watch?v=JAGDpJFFM2A)

- [Weaponizing Data Science for Social Engineering](https://www.youtube.com/watch?v=l7U0pDcsKLg)

- [Crowd Trained Machine Learning Model for Malware Capability Det](https://www.youtube.com/watch?v=u6a7afsD39A)

- [Defeating Machine Learning: Systemic Deficiencies for Detecting Malware](https://www.youtube.com/watch?v=sPtbDUJjhbk)

- [Packet Capture Village - Theodora Titonis - How Machine Learning Finds Malware](https://www.youtube.com/watch?v=2cQRSPFSY-s)

- [Build an Antivirus in 5 Min - Fresh Machine Learning](https://www.youtube.com/watch?v=iLNHVwSu9EA&t=245s)

- [Machine Learning and Cyber Security](https://www.youtube.com/watch?v=qVwktOa-F34)

- [Machine Learning and the Cloud: Disrupting Threat Detection and Prevention](https://www.youtube.com/watch?v=fRklX97iGIw)

- [Fraud detection using machine learning & deep learning ](https://www.youtube.com/watch?v=gHtN4jU69W0)

- [Defending Networks With Incomplete Information: A Machine Learning Approach](https://www.youtube.com/watch?v=_0CRSF6yPB4)

- [Machine Learning & Data Science](https://vimeo.com/112702666)

- [Advances in Cloud-Scale Machine Learning for Cyber-Defense](https://www.youtube.com/watch?v=skSIIvvZFIk)

- [Applied Machine Learning: Defeating Modern Malicious Documents](https://www.youtube.com/watch?v=ZAuCEgA3itI)

- [FeatureSmith: Learning to Detect Malware by Mining the Security Literature](https://www.youtube.com/watch?v=ikaDWJhSMIU&feature=youtu.be)



## [↑](#Contents)Tutorials

- [Big Data and Data Science for Security and Fraud Detection](http://www.kdnuggets.com/2015/12/big-data-science-security-fraud-detection.html)

- [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/)

- [Data mining for network security and intrusion detection](https://www.r-bloggers.com/data-mining-for-network-security-and-intrusion-detection/)

- [Applying Machine Learning to Improve Your Intrusion Detection System](https://securityintelligence.com/applying-machine-learning-to-improve-your-intrusion-detection-system/)

- [Analyzing BotNets with Suricata & Machine Learning](http://blogs.splunk.com/2017/01/30/analyzing-botnets-with-suricata-machine-learning/)

- [Deep Session Learning for Cyber Security](https://blog.cyberreboot.org/deep-session-learning-for-cyber-security-e7c0f6804b81#.eo2m4alid)

- [Data Mining for Cyber Security](http://web.stanford.edu/class/cs259d/)

- [Data Science and Machine Learning for Infosec](http://www.pentesteracademy.com/course?id=30)



## [↑](#Contents)Open Source Project

- [Apache Spot](http://spot.incubator.apache.org/)

-       



## [↑](#Contents)Links Domain

- [Blackhat](https://www.blackhat.com/)

- [Coursera](https://www.coursera.org/)

- [FastAI](https://www.fast.ai/)

- [AI News](https://artificialintelligence-news.com/)

- [MIT News](http://news.mit.edu/topic/artificial-intelligence2)

- [DeepMind](https://deepmind.com/)

- [OpenAI](https://openai.com/)

- [AIResources](http://www.airesources.info/)

- [Awesome Hacking](https://github.com/Hack-with-Github/Awesome-Hacking)

- [Awesome Machine Learning for Cyber Security](https://github.com/jivoi/awesome-ml-for-cybersecurity)

- [Awesome AI Security](https://github.com/RandomAdversary/Awesome-AI-Security)



## [↑](#Contents)References for reseacher

[1] [SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks](https://arxiv.org/ftp/arxiv/papers/1811/1811.01443.pdf)


[2] [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)


[3] [Distributed Representations of Words and Phrases

and their Compositionality](http://cn.arxiv.org/pdf/1310.4546v1)


[4] [Improving Adversarial Robustness by Encouraging Discriminative Features](https://arxiv.org/pdf/1811.00621.pdf)


[5] [AutoEncoder by Forest](https://arxiv.org/pdf/1709.09018.pdf)


[6] [Malicious Web Request Detection Using

Character-level CNN](https://arxiv.org/pdf/1811.08641.pdf)


[7] [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf)


[8] [Sliced Recurrent Neural Networks](https://arxiv.org/ftp/arxiv/papers/1807/1807.02291.pdf)


[9] [Detecting malware domains at the upper dns hierarchy](http://www.covert.io/research-papers/security/Kopis%20-%20Detecting%20malware%20domains%20at%20the%20upper%20dns%20hierarchy.pdf)


[10] [Detecting the Rise of DGA-Based Malware](http://www.covert.io/research-papers/security/From%20throw-away%20traffic%20to%20bots%20-%20detecting%20the%20rise%20of%20dga-based%20malware.pdf)


[11] [Finding Malicious Domains Using Passive DNS Analysis](http://www.covert.io/research-papers/security/Exposure%20-%20Finding%20malicious%20domains%20using%20passive%20dns%20analysis.pdf)


[12] [ Tera-Scale Graph Mining for Malware Detection](http://www.covert.io/research-papers/security/Polonium%20-%20Tera-Scale%20Graph%20Mining%20for%20Malware%20Detection.pdf)


[13] [Detecting Malware Distribution in Large-Scale Networks](http://www.covert.io/research-papers/security/Nazca%20-%20%20Detecting%20Malware%20Distribution%20in%20Large-Scale%20Networks.pdf)


[14] [Anomalous Payload-based Network Intrusion Detection](http://www.covert.io/research-papers/security/PAYL%20-%20Anomalous%20Payload-based%20Network%20Intrusion%20Detection.pdf)


[15] [ A Content Anomaly Detector Resistant to Mimicry Attack](http://www.covert.io/research-papers/security/Anagram%20-%20A%20Content%20Anomaly%20Detector%20Resistant%20to%20Mimicry%20Attack.pdf)


[16] [Applications of Machine Learning in Cyber Security](https://www.researchgate.net/publication/283083699_Applications_of_Machine_Learning_in_Cyber_Security)


[17] [Dimension Reduction in Network Attacks Detection Systems](http://elib.bsu.by/bitstream/123456789/120105/1/v17no3p284.pdf)


[18] [Modeling Password Guessability Using Neural Networks](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher)


[19] [On Using Machine Learning for Network Intrusion Detection](http://ieeexplore.ieee.org/document/5504793/?reload=true)


[20] [Anomalous Payload-Based Network Intrusion Detection](https://link.springer.com/chapter/10.1007/978-3-540-30143-1_11)


[21] [Malicious PDF detection](http://dl.acm.org/citation.cfm?id=2420987)


[22] [Adversarial support vector machine learning](https://dl.acm.org/citation.cfm?id=2339697)


[23] [Exploiting machine learning to subvert your spam filter](https://dl.acm.org/citation.cfm?id=1387709.1387716)


[24] [Content-Agnostic Malware Protection](http://www.covert.io/research-papers/security/CAMP%20-%20Content%20Agnostic%20Malware%20Protection.pdf)


[25] [Building a dynamic reputation system for dns](http://www.covert.io/research-papers/security/Notos%20-%20Building%20a%20dynamic%20reputation%20system%20for%20dns.pdf)


[26] [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805.pdf) 



## [↑](#Contents)Deep Learning Papers

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832), F. Schroff et al., arXiv, 2015.

- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385), K. He et al., arXiv, 2015.

- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261), C. Szegedy et al., arXiv, 2016.

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), A. Radford et al., arXiv, 2015.

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497), S. Ren et al., arXiv, 2015.

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), A. Radford et al., arXiv, 2015.



## [↑](#Contents)Deep Reinforcement Learning Papers

- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://arxiv.org/abs/1507.06527),M. Hausknecht and P. Stone, arXiv, 2015.

- [How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies](https://arxiv.org/abs/1512.02011), V. François-Lavet et al., NIPS Workshop, 

- [Deep Exploration via Bootstrapped DQN](https://arxiv.org/abs/1602.04621), I. Osband et al., arXiv, 2016.

- [Maximum Entropy Deep Inverse Reinforcement Learning](https://arxiv.org/abs/1507.04888), M. Wulfmeier et al., arXiv, 2015.

- [Value Iteration Networks](https://arxiv.org/abs/1602.02867), A. Tamar et al., arXiv, 2016.

- [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., NIPS Workshop, 2013.

- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://arxiv.org/abs/1506.08941), K. Narasimhan et al., EMNLP, 2015.

- [Safe and Efficient Off-Policy Reinforcement Learning](https://arxiv.org/abs/1606.02647), R. Munos et al., arXiv, 2016.

- [Dynamic Frame skip Deep Q Network](https://arxiv.org/abs/1605.05365), A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.

- [Control of Memory, Active Perception, and Action in Minecraft](https://arxiv.org/abs/1605.09128), J. Oh et al., ICML, 2016.

- [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461), H. van Hasselt et al., arXiv, 2015.

- [Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning](https://arxiv.org/abs/1901.02219), A. Sedlmeier et al., arXiv, 2019.

- [Recurrent Reinforcement Learning: A Hybrid Approach](https://arxiv.org/abs/1509.03044), X. Li et al., arXiv, 2015.

- [Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks](https://arxiv.org/abs/1809.06784), A. Gupta et al, arXiv, 2018.

- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://arxiv.org/abs/1803.01164), M.Z. Alom et al., arXiv, 2018.

- [Unifying Count-Based Exploration and Intrinsic Motivation](https://arxiv.org/abs/1606.01868), M. G. Bellemare et al., arXiv, 2016.

- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://arxiv.org/abs/1604.06778), Y. Duan et al., ICML, 2016.



## [↑](#Contents)License

[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](http://creativecommons.org/publicdomain/zero/1.0)

To the extent possible under law, [Noa Swartz](https://github.com/fetaxyu) has waived all copyright and related or neighboring rights to this work.