Conference on Workshop on Hot Topics in Understanding Botnets · 2007 · 323 citations · 8 references
ComputingComputer ScienceCybersecurityApplication SecurityComputer SecurityNetwork SecurityBot DetectionBotnet AnalysisDenial-of-service AttacksIntrusion DetectionNetwork Traffic AnalysisThreat DetectionAlertsApplication LayerBotnet Behavior AnalysisBotnet DetectionBotnet Traffic AnalysisBotnet Traffic DetectionDenial-of-service Botnet AnalysisPhishing
Malicious botnets are networks of compromised computers that are controlled remotely to perform large‑scale distributed denial‑of‑service attacks, send spam, trojan and phishing emails, distribute pirated media, or conduct other illicit activities. This paper presents a methodology to detect, track, and characterize botnets on a large Tier‑1 ISP network. The authors employ scalable, non‑intrusive algorithms that analyze vast summary traffic data from selected network links, focusing mainly on transport‑layer data to avoid dependence on application‑layer details. The algorithms generate alerts identifying botnet controllers, and subsequent application‑layer analysis shows a false‑positive rate of less than 2%.
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.
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