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Detecting Stealthy P2P Botnets Using Statistical Traffic Fingerprints
Proceedings of the International Conference on Dependable Systems and Networks
  • Junjie Zhang, Wright State University - Main Campus
  • Roberto Perdisci
  • Wenke Lee
  • Unum Sarfraz
  • Xiapu Luo
Document Type
Conference Proceeding
Publication Date
6-1-2011
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Abstract
Peer-to-peer (P2P) botnets have recently been adopted by botmasters for their resiliency to 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, including, ineffective. In this paper, we propose a novel botnet detection system that is able to identify stealthy P2P botnets, even when malicious activities may not be observable. First, our system identifies all hosts that are likely engaged in P2P communications. Then, we derive statistical fingerprints to profile different types of P2P traffic, and we leverage these fingerprints to distinguish between P2P botnet traffic and other legitimate P2P traffic. Unlike previous work, our system is able to detect stealthy P2P botnets even when the underlying compromised hosts are running legitimate P2P applications (e.g., Skype) and the P2P bot software at the same time. Our experimental evaluation based on real-world data shows that the proposed system can achieve high detection accuracy with a low false positive rate.
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Presented at the 41st Annual IEEE/IFIP International Conference on Dependable Systems & Networks, Hong Kong, China.

DOI
10.1109/DSN.2011.5958212
Citation Information
Junjie Zhang, Roberto Perdisci, Wenke Lee, Unum Sarfraz, et al.. "Detecting Stealthy P2P Botnets Using Statistical Traffic Fingerprints" Proceedings of the International Conference on Dependable Systems and Networks (2011) p. 121 - 132 ISSN: 1530-0889
Available at: http://works.bepress.com/junjie_zhang/7/