Botyacc: Unified P2P botnet detection using behavioural analysis and graph analysis

Abstract

The detection and isolation of peer-to-peer botnets is an ongoing problem. We propose a novel technique for detecting P2P botnets. Detection is based on unifying behavioural analysis with structured graph analysis. First, our inference technique exploits a fundamental property of botnet design. Modern botnets use peer-to-peer communication topologies which are fundamental to botnet resilience. Second, our technique extends conventional graph-based detection by incorporating behavioural analysis into structured graph analysis, thus unifying graph-theoretic detection with behavioural detection under a single algorithmic framework. We carried out evaluation over real-world P2P botnet traffic and show that the resulting algorithm can localise the majority of bots with low false-positive rate. © 2014 Springer International Publishing Switzerland.