https://github.com/twseptian/anomaly-detection-analysis

https://github.com/twseptian/anomaly-detection-analysis

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/twseptian/anomaly-detection-analysis
• Owner: twseptian
• Created: 2018-07-06T18:00:41.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-07-10T03:28:52.000Z (about 7 years ago)
• Last Synced: 2025-05-15T01:40:04.720Z (5 months ago)
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Awesome Machine Learning for Anomaly Detection: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of machine learning for anomaly detection resources, inspired by [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) and [Awesome Architecture Search](https://github.com/markdtw/awesome-architecture-search/blob/master/README.md)

## Table of Contents

  - [Papers](#papers)

  - [Datasets](#datasets)

## Papers

  * [Real-time DDoS attack detection for Cisco IOS using NetFlow](http://ieeexplore.ieee.org/abstract/document/7140420/), D. van der Steeg et al., IFIP/IEEE IM 2015

  * [A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152173), M. Goldstein et al., PloS one, 2016

  * [Detection DDoS attacks based on neural-network using Apache Spark](http://ieeexplore.ieee.org/abstract/document/7539833/), C. Hsieh et al., ICASI, 2016

  * [Large-scale IP network behavior anomaly detection and identification using substructure-based approach and multivariate time series mining](https://link.springer.com/article/10.1007/s11235-010-9384-1), W. He et al., Telecommunication Systems, 2012

  * [A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection](http://ieeexplore.ieee.org/abstract/document/7307098/), A. Buczak et al., IEEE Communications Surveys & Tutorials 2016

  * [An Abnormal Network Traffic Detection Algorithm Based on Big Data Analysis](http://eds.a.ebscohost.com/eds/detail/detail?vid=0&sid=63eb62fc-fcc1-4602-ad09-fdf12cca069c%40sessionmgr4006&bdata=Jmxhbmc9emgtdHcmc2l0ZT1lZHMtbGl2ZSZzY29wZT1zaXRl#AN=116795190&db=asr), H. Yao et al., International Journal of Computers, Communications & Control 2016

  * [Big data analytics for network anomaly detection from netflow data](http://ieeexplore.ieee.org/abstract/document/8093473/), D. Terzi et al., UBMK 2017

  * [FLAME: A Flow-Level Anomaly Modeling Engine](https://www.usenix.org/legacy/event/cset08/tech/full_papers/brauckhoff/brauckhoff_html/), D. Brauckhoff et al., CSET, 2008

  * [Characterizing network traffic by means of the NETMINE framework](https://www.sciencedirect.com/science/article/pii/S1389128608004052), D. Apiletti et al., Computer Networks, 2009

  * [Machine Learning Approach for IP-Flow Record Anomaly Detection](https://link.springer.com/10.1007%2F978-3-642-20757-0_3), C. Wagner et al., ICRN, 2011

  * [Outside the Closed World/ On Using Machine Learning For Network Intrusion Detection](http://ieeexplore.ieee.org/abstract/document/5504793/), R. Sommer et al., IEEE SP, 2010

  * [Machine Learning Techniques for Anomaly Detection: An Overview](https://pdfs.semanticscholar.org/0278/bbaf1db5df036f02393679d485260b1daeb7.pdf), S. Omar et al., IJCA, 2013

  * [Detection of known and unknown DDoS attacks using Artificial Neural Networks](https://www.sciencedirect.com/science/article/pii/S092523121501053X), A. Saied et al., Neurocomputing, 2015

  * [An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks](https://pdfs.semanticscholar.org/636f/4733c2851f5bcdab14481c18bb3aca66ccb2.pdf), R. Karimazad et al., ICNEE, 2011

  * [A data mining framework for building intrusion detection models](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=766909), W. Lee et al., IEEE SP, 1999

## Datasets

  * [DARPA](https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-data-set)

  * [KDD Cup 1999](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)

  * [ISCX IDS](http://www.unb.ca/cic/datasets/ids.html)

## Licenses

License

[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, I have waived all copyright and related or neighboring rights to this work.