Behavior Abstraction in Malware Analysis - Extended Version

Philippe Beaucamps 1, * Isabelle Gnaedig 1 Jean-Yves Marion 1
* Auteur correspondant
1 CARTE - Theoretical adverse computations, and safety
Inria Nancy - Grand Est, LORIA - FM - Department of Formal Methods
Abstract : We present an approach for proactive malware detection by working on an abstract representation of a program behavior. Our technique consists in abstracting program traces, by rewriting given subtraces into abstract symbols representing their functionality. Traces are captured dynamically by code instrumentation in order 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.
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Pré-publication, Document de travail
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Contributeur : Isabelle Gnaedig <>
Soumis le : samedi 23 octobre 2010 - 18:50:57
Dernière modification le : mardi 18 décembre 2018 - 16:48:02
Document(s) archivé(s) le : vendredi 2 décembre 2016 - 10:38:37


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  • HAL Id : inria-00509486, version 2



Philippe Beaucamps, Isabelle Gnaedig, Jean-Yves Marion. Behavior Abstraction in Malware Analysis - Extended Version. 2010. 〈inria-00509486v2〉



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