Measuring the effectiveness of infrastructure-level detection of large-scale botnets

  • Authors:
  • Yuanyuan Zeng;Guanhua Yan;Stephan Eidenbenz;Kang G. Shin

  • Affiliations:
  • University of Michigan;Los Alamos National Laboratory;Los Alamos National Laboratory;University of Michigan

  • Venue:
  • Proceedings of the Nineteenth International Workshop on Quality of Service
  • Year:
  • 2011

Quantified Score

Hi-index 0.00



Botnets are one of the most serious security threats to the Internet and its end users. In recent years, utilizing P2P as a Command and Control (C&C) protocol has become popular due to its decentralized nature that can help hide the botmaster's identity. Most bot detection approaches targeting P2P botnets either rely on behavior monitoring or traffic flow and packet analysis, requiring fine-grained information collected locally. This requirement limits the scale of detection. In this paper, we consider detection of P2P botnets at a high-level---the infrastructure level---by exploiting their structural properties from a graph analysis perspective. Using three different P2P overlay structures, we measure the effectiveness of detecting each structure at various locations (the Autonomous System (AS), the Point of Presence (PoP), and the router rendezvous) in the Internet infrastructure.