Detection of stealthy malware activities with traffic causality and scalable triggering relation discovery

Hao Zhang, Naren Ramakrishnan

Abstract

Studies show that a significant portion of networked computers are infected with stealthy malware. Infection allows remote attackers to control, utilize, or spy on victim machines. Conventional signature-scan or counting-based techniques are limited, as they are unable to stop new zero-day exploits. We describe a traffic analysis method that can effectively detect malware activities on a host. Our new approach efficiently discovers the underlying triggering relations of a massive amount of network events. We use these triggering relations to reason the occurrences of network events and to pinpoint stealthy malware activities. We define a new problem of triggering relation discovery of network events. Our solution is based on domain-knowledge guided advanced learning algorithms. Our extensive experimental evaluation involving 6+ GB traffic of various types shows promising results on the accuracy of our triggering relation discovery.