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CS6262-O01 Network Security – Project 5 Training & Evading ML based IDS 1 Introduction/Assignment Goal The goal of this project is to introduce students to machine learning techniques and methodologies, that help to differentiate between malicious and legitimate network traffic. In summary, the students are introduced to: • Use a machine learning based approach to create a model that learns normal network traffic. • Learn how to blend attack traffic, so that it resembles normal network traffic, and by-pass the learned model. NOTE: To work on this project, we recommend you to use Linux OS. However, in the past, students faced no difficulty while working on this project even on Windows or Macintosh OS. 2 Readings & Resources This assignment relies on the following readings: • “Anomalous Payload-based Worm Detection and Signature Generation”, Ke Wang, Gabriela Cretu, Salvatore J. Stolfo, RAID2004. • “Polymorphic Blending Attacks”, Prahlad Fogla, Monirul Sharif, Roberto Perdisci, Oleg Kolesnikov, Wenke Lee, Usenix Security 2006. • “True positive (true detections) and False positive (false alarms)” 3 Task A • Preliminary reading. Please refer to the above readings to learn about how the PAYL model works: a) how to extract byte frequency from the data, b) how to train the model, and c) the definition of the parameters; threshold and smoothing factor. Note: Without this background it will be very hard to follow through the tasks. • Code and data provided. Please look at the PAYL directory, where we provide the PAYL code and data to train the model. • Install packages needed. Please read the file SETUP to install packages that are needed for the code to run. • PAYL Code workflow. Here is the workf
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