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Learning and Classification of Malware Behavior

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNSC,volume 5137)

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.

Keywords

  • Normalize Compression Distance
  • Internet Worm
  • Unknown Behavior
  • 15th USENIX Security Symposium
  • Benign Executable

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-70542-0_6
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Rieck, K., Holz, T., Willems, C., Düssel, P., Laskov, P. (2008). Learning and Classification of Malware Behavior. In: Zamboni, D. (eds) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2008. Lecture Notes in Computer Science, vol 5137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70542-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-70542-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70541-3

  • Online ISBN: 978-3-540-70542-0

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