Meta-framework for Automating Static Malware Analysis

In cybercrime, malware plays a weighty role and malware authors heavily rely on different code obfuscation techniques such as packing, virtualisation, or control flow transformations, and other anti-analysis methods to hide malicious functionality in binary code. With thousands of new malware samples emerging every day, efficient analysis is crucial for fighting malware-based cybercrime. We present a novel meta-framework for malware analysis that helps find the optimal analysis strategy for a malware sample. The research for the work was conducted in a joint project together with the University of Gent in Belgium [L1].