Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8091
Title: Building a scalable system for stealthy P2P-botnet detection
Authors: Zhang, J
Perdisci, R
Lee, W
Luo, X 
Sarfraz, U
Keywords: Botnet
Intrusion detection
Network security
P2P
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information forensics and security, 2014, v. 9, no. 1, 6661360, p. 27-38 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: Peer-to-peer (P2P) botnets have recently been adopted by botmasters for their resiliency against take-down efforts. Besides being harder to take down, modern botnets tend to be stealthier in the way they perform malicious activities, making current detection approaches ineffective. In addition, the rapidly growing volume of network traffic calls for high scalability of detection systems. In this paper, we propose a novel scalable botnet detection system capable of detecting stealthy P2P botnets. Our system first identifies all hosts that are likely engaged in P2P communications. It then derives statistical fingerprints to profile P2P traffic and further distinguish between P2P botnet traffic and legitimate P2P traffic. The parallelized computation with bounded complexity makes scalability a built-in feature of our system. Extensive evaluation has demonstrated both high detection accuracy and great scalability of the proposed system.
URI: http://hdl.handle.net/10397/8091
ISSN: 1556-6013
EISSN: 1556-6021
DOI: 10.1109/TIFS.2013.2290197
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