Anomaly Detection and Machine Learning

<p>The datasets were preprocessed. Correlated features were removed.</p> <p>Related papers:</p> <p><strong>[1]</strong>&nbsp;Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, &ldquo;Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization&rdquo;, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018</p> <p><strong>[2]</strong>&nbsp;Nour Moustafa, October 16, 2019, &quot;UNSW_NB15 dataset&quot;, IEEE Dataport, doi: https://dx.doi.org/10.21227/8vf7-s525.</p> <p><strong>[3]</strong> &ldquo;Sebastian Garcia, Agustin Parmisano, &amp; Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746&rdquo;</p> <p><strong>[4]</strong>&nbsp; A. D. Kent, &ldquo;Comprehensive, Multi-Source Cybersecurity Events,&rdquo; Los Alamos National Laboratory, http://dx.doi.org/10.17021/1179829, 2015.</p> <p>&nbsp;</p>