PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning and Classification of Malware Behavior
Konrad Rieck, Thorsten Holz, Carsten Willems, Patrick Düssel and Pavel Laskov
Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) pp. 108-125, 2008.

Abstract

Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patterns reflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifier is trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with different heterogeneous test data collected over several months using honeypots demonstrate the effectiveness of our method, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:4171
Deposited By:Konrad Rieck
Deposited On:09 October 2008