Abstract:
Botnets have become one of the major
threats on the Internet. They are used to generate
spam, carry out DDOS (Distributed Denial of
Service) attacks and click-fraud, and steal sensitive
information. Nowadays, many researchers interest to
analyze the botnet technology and emphasis the
botnet behaviors. It is needed to classify communication
network traffic which is important fact to
study the botnet behaviors. In this paper, we proposed
an approach to detect botnet activity by analyzing and
classifying network traffic behaviors due to P2P (Peer
to Peer) based botnets. This system represents the
important and most challenging types of botnet
currently available that based on classifying P2P
botnets. The classification techniques used in
detection framework are RF (Random Forest) and
SVM (Support Vector Machine). The performance
evaluation of the two popular classification
techniques is also presented. According to the
experiments, proposed system has promising accuracy
even with small time window by comparing two
machine learning algorithms.
