A24: Comparison of Machine Learning Algorithms and Their Ensembles for Botnet Detection
Student: Songhui Ryu (Purdue University)
Supervisor: Preston Smith (Purdue University)
Abstract: A Botnet is a network of compromised devices that is controlled by malicious ‘botmaster’ in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect Botnet traffic can be a promising feature of Intrusion Detection System(IDS). Although IDS has been applying various machine learning (ML) techniques, comparison of ML algorithms including their ensembles on Botnet detection has not been figured out yet. In this study, not only the three most popular classification ML algorithms – Naïve Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen ML algorithms are tested to see if they indeed provide enhanced predictions on Botnet detection. This evaluation is conducted with CTU-13 public dataset, measuring running time of each ML and its f measure and MCC score.
ACM-SRC Semi-Finalist: no
Poster: pdf
Two-page extended abstract: pdf
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