The popularity of Peer-to-peer (P2P) applications have shown a massive growth in recent times, and P2P traffic contributes considerably to the today's internet traffic. For efficient network traffic management and effective malware detection, P2P traffic classification is indispensable. This paper proposes LASER, Longest Common Subsequence (LCS)-based Application Signature ExtRaction technique, algorithm, a novel hybrid network traffic classification technique which classifies the P2P traffic into malicious P2P and non-malicious P2P traffic. The proposed classifier analyzes the header information for creating a communication module. Further, the signature is extracted from the payload information. We build the classifier by aggregating the information of header and the payload. The proposed hybrid classifier is analyzed for its performance and the results are promising.
Dr. Sajeev G. P. and Nair, L. M., “LASER: A novel hybrid peer to peer network traffic classification technique”, in Fourth International Symposium on Women in Computing and Informatics (WCI-2016), International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.