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Support vector machines and malware detection
Journal of Computer Virology and hacking Techniques (2016)
  • Mark Stamp, San Jose State University
  • Tanuvir Singh, San Jose State University
  • Fabio Di Troia, Università degli Studi del Sannio
  • Visaggio A. Corrado, Università degli Studi del Sannio
  • Thomas H. Austin, San Jose State University
In this research, we test three advanced malware scoring techniques that have shown promise in previous research, namely, Hidden Markov Models, Simple Substitution Distance, and Opcode Graph based detection. We then perform a careful robustness analysis by employing morphing strategies that cause each score to fail. We show that combining scores using a Support Vector Machine yields results that are significantly more robust than those obtained using any of the individual scores.
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Citation Information
Mark Stamp, Tanuvir Singh, Fabio Di Troia, Visaggio A. Corrado, et al.. "Support vector machines and malware detection" Journal of Computer Virology and hacking Techniques Vol. 12 Iss. 4 (2016) p. 203 - 212 ISSN: 2274-2042
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