A curated list of machine learning for anomaly detection resources, inspired by Awesome Adversarial Machine Learning and Awesome Architecture Search
- Real-time DDoS attack detection for Cisco IOS using NetFlow, D. van der Steeg et al., IFIP/IEEE IM 2015
- A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data, M. Goldstein et al., PloS one, 2016
- Detection DDoS attacks based on neural-network using Apache Spark, C. Hsieh et al., ICASI, 2016
- Large-scale IP network behavior anomaly detection and identification using substructure-based approach and multivariate time series mining, W. He et al., Telecommunication Systems, 2012
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection, A. Buczak et al., IEEE Communications Surveys & Tutorials 2016
- An Abnormal Network Traffic Detection Algorithm Based on Big Data Analysis, H. Yao et al., International Journal of Computers, Communications & Control 2016
- Big data analytics for network anomaly detection from netflow data, D. Terzi et al., UBMK 2017
- FLAME: A Flow-Level Anomaly Modeling Engine, D. Brauckhoff et al., CSET, 2008
- Characterizing network traffic by means of the NETMINE framework, D. Apiletti et al., Computer Networks, 2009
- Machine Learning Approach for IP-Flow Record Anomaly Detection, C. Wagner et al., ICRN, 2011
- Outside the Closed World/ On Using Machine Learning For Network Intrusion Detection, R. Sommer et al., IEEE SP, 2010
- Machine Learning Techniques for Anomaly Detection: An Overview, S. Omar et al., IJCA, 2013
- Detection of known and unknown DDoS attacks using Artificial Neural Networks, A. Saied et al., Neurocomputing, 2015
- An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks, R. Karimazad et al., ICNEE, 2011
- A data mining framework for building intrusion detection models, W. Lee et al., IEEE SP, 1999
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