Date of Award


Degree Type


Degree Name

Master of Science (MS)


Computer and Information Technology

Committee Chair

John Springer

Committee Member 1

Baijian Yang

Committee Member 2

Eric Matson


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.