Behavior Abstraction in Malware Analysis

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Abstract

We present an approach for proactive malware detection working by abstraction of program behaviors. Our technique consists in abstracting program traces, by rewriting given subtraces into abstract symbols representing their functionality. Traces are captured dynamically by code instrumentation, which allows us to handle packed or self-modifying malware. Suspicious behaviors are detected by comparing trace abstractions to reference malicious behaviors. The expressive power of abstraction allows us to handle general suspicious behaviors rather than specific malware code and then, to detect malware mutations. We present and discuss an implementation validating our approach.

This work has been partially supported by the High Security Lab of the LORIA in Nancy: http://lhs.loria.fr. A full version of this article can be found in [5].