A24: Comparison of Machine Learning Algorithms and Their
Ensembles for Botnet Detection
SessionPoster Reception
Author
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionA 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.
Author
