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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
:octocat: Machine Learning for Cyber Security
| Date | Stars |
|---|---|
| 2026-07-31 | 9225 |
| 2026-08-01 | 9225 |
| 2026-08-02 | 9234 |
| 2026-08-06 | 9242 |
Today
+8 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome Machine Learning for Cyber Security [](https://github.com/sindresorhus/awesome) [<img src="https://github.com/jivoi/awesome-ml-for-cybersecurity/raw/master/cyber-ml-logo.png" align="right" width="100">](https://github.com/jivoi/awesome-ml-for-cybersecurity) A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security. ## Table of Contents - [Datasets](#-datasets) - [Papers](#-papers) - [Books](#-books) - [Talks](#-talks) - [Tutorials](#-tutorials) - [Courses](#-courses) - [Miscellaneous](#-miscellaneous) ## [↑](#table-of-contents) Contributing Please read [CONTRIBUTING](./CONTRIBUTING.md) if you wish to add tools or resources. ## [↑](#table-of-contents) Datasets * [HIKARI-2021 Datasets](https://zenodo.org/record/5199540) * [Samples of Security Related Data](http://www.secrepo.com/) * [DARPA Intrusion Detection Data Sets](https://www.ll.mit.edu/r-d/datasets) [ [1998](https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset) / [1999](https://www.ll.mit.edu/r-d/datasets/1999-darpa-intrusion-detection-evaluation-dataset) ] * [Stratosphere IPS Data Sets](https://stratosphereips.org/category/dataset.html) * [Open Data Sets](http://csr.lanl.gov/data/) * [Data Capture from National Security Agency](http://www.westpoint.edu/crc/SitePages/DataSets.aspx) * [The ADFA Intrusion Detection Data Sets](https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-IDS-Datasets/) * [NSL-KDD Data Sets](https://github.com/defcom17/NSL_KDD) * [Malicious URLs Data Sets](http://sysnet.ucsd.edu/projects/url/) * [Multi-Source Cyber-Security Events](http://csr.lanl.gov/data/cyber1/) * [KDD Cup 1999 Data](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html) * [Web Attack Payloads](https://github.com/foospidy/payloads) * [WAF Malicious Queries Data Sets](https://github.com/faizann24/Fwaf-Machine-Learning-driven-Web-Application-Firewall) * [Malware Training Data Sets](https://github.com/marcoramilli/MalwareTrainingSets) * [Aktaion Data Sets](https://github.com/jzadeh/Aktaion/tree/master/data) * [CRIME Database from DeepEnd Research](https://www.dropbox.com/sh/7fo4efxhpenexqp/AADHnRKtL6qdzCdRlPmJpS8Aa/CRIME?dl=0) * [Publicly available PCAP files](http://www.netresec.com/?page=PcapFiles) * [2007 TREC Public Spam Corpus](https://plg.uwaterloo.ca/~gvcormac/treccorpus07/) * [Drebin Android Malware Dataset](https://www.sec.cs.tu-bs.de/~danarp/drebin/) * [PhishingCorpus Datset](https://monkey.org/~jose/phishing/) * [EMBER](https://github.com/endgameinc/ember) * [Vizsec Research](https://vizsec.org/data/) * [SHERLOCK](http://bigdata.ise.bgu.ac.il/sherlock/index.html#/) * [Probing / Port Scan - Dataset ](https://github.com/gubertoli/ProbingDataset) * [Aegean Wireless Intrusion Dataset (AWID)](http://icsdweb.aegean.gr/awid/) * [BODMAS PE Malware Dataset](https://whyisyoung.github.io/BODMAS/) ## [↑](#table-of-contents) Papers * [Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic](https://www.mdpi.com/2076-3417/11/17/7868/htm) * [Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher) * [Outside the Closed World: On Using Machine Learning for Network Intrusion Detection](http://ieeexplore.ieee.org/document/5504793/?reload=true) * [Anomalous Payload-Based Network Intrusion Detection](https://link.springer.com/chapter/10.1007/978-3-540-30143-1_11) * [Malicious PDF detection using metadata and structural features](http://dl.acm.org/citation.cfm?id=2420987) * [Adversarial support vector machine learning](https://dl.acm.org/citation.cfm?id=2339697) * [Exploiting machine learning to subvert your spam filter](https://dl.acm.org/citation.cfm?id=1387709.1387716) * [CAMP –
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Gustavo Bertoli · Germany
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Kim Hammar · Imperial College · United Kingdom
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Siddharth Satpathy · Element Energy · United States
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jose nazario
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f69c0f2768b5d607, topic:awesome-list, readme:curated list