Date of Award
5-2018
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer and Information Technology
Committee Chair
John Springer
Committee Member 1
Baijian Yang
Committee Member 2
Eric Matson
Abstract
A Botnet is a network of compromised devices controlled by a botmaster often for nefarious purposes. Analyzing network traffc to detect Botnet traffc has historically been an effective approach for systems monitoring for network intrusion. Although such system have been applying various machine learning techniques, little investigation into a comparison of machine algorithms and their ensembles has been undertaken. In this study, three popular classifcation machine learning algorithms – Naive Bayes, Decision tree, and Neural network – as well as the ensemble methods known to strengthen said classifers are evaluated for enhanced results related to Botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time and accuracy scores of each classifer.
Recommended Citation
Ryu, Songhui, "Comparison of Machine Learning Algorithms and Their Ensembles for Botnet Detection" (2018). Open Access Theses. 1451.
https://docs.lib.purdue.edu/open_access_theses/1451