Wide-scale botnet detection and characterization

  • Authors:
  • Anestis Karasaridis;Brian Rexroad;David Hoeflin

  • Affiliations:
  • AT&T Labs, Middletown, NJ;AT&T Chief Security Officer, Florham Park, NJ;AT&T Labs, Middletown, NJ

  • Venue:
  • HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
  • Year:
  • 2007

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Abstract

Malicious botnets are networks of compromised computers that are controlled remotely to perform large-scale distributed denial-of-service (DDoS) attacks, send spam, trojan and phishing emails, distribute pirated media or conduct other usually illegitimate activities. This paper describes a methodology to detect, track and characterize botnets on a large Tier-1 ISP network. The approach presented here differs from previous attempts to detect botnets by employing scalable non-intrusive algorithms that analyze vast amounts of summary traffic data collected on selected network links. Our botnet analysis is performed mostly on transport layer data and thus does not depend on particular application layer information. Our algorithms produce alerts with information about controllers. Alerts are followed up with analysis of application layer data, that indicates less than 2% false positive rates.