Malware traffic detection using tamper resistant features

Abstract

This paper presents a framework for evaluating the transport layer feature space of malware heartbeat traffic. We utilize these features in a prototype detection system to distinguish malware traffic from traffic generated by legitimate applications. In contrast to previous work, we eliminate features at risk of producing overly optimistic detection results, detect previously unobserved anomalous behavior, and rely only on tamper-resistant features making it difficult for sophisticated malware to avoid detection. Further, we characterize the evolution of malware evasion techniques over time by examining the behavior of 16 malware families. In particular, we highlight the difficultly of detecting malware that use traffic-shaping techniques to mimic legitimate traffic.