ASAP: Automatic Semantics-Aware Analysis of Network Payloads
Tammo Krüger, Nicole Krämer and Konrad Rieck
European Conference on Machine Learning, Workshop on Privacy and Security Issues in Data Mining and Machine Learning
Automatic inspection of network payloads is a prerequisite for effective analysis of network communication. Security research has largely focused on network analysis using protocol specifications, for example for intrusion detection, fuzz testing and forensic analysis. The specification of a protocol alone, however, is often not sufficient for accurate analysis of communication, as it fails to reflect individual semantics of network applications. We propose a framework for semantics-aware analysis of network payloads which automaticylly extracts semantic components from recorded network traffic. Our method proceeds by mapping network payloads to a vector space and identifying semantic templates corresponding to base directions in the vector space. We demonstrate the efficacy of semantics-aware analysis in different security applications: automatic discovery of patterns in honeypot data, analysis of malware communication and network intrusion detection.