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A comparison of static, dynamic, and hybrid analysis for malware detection

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Journal of Computer Virology and Hacking Techniques Aims and scope Submit manuscript

Abstract

In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques.

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Notes

  1. For example, if one curve dominates another in ROC space, it also dominates in PR space, and vice versa.

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Authors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, USA

    Anusha Damodaran, Thomas H. Austin & Mark Stamp

  2. Department of Engineering, Università degli Studi del Sannio, Benevento, Italy

    Fabio Di Troia & Corrado Aaron Visaggio

Authors
  1. Anusha Damodaran
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  2. Fabio Di Troia
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  3. Corrado Aaron Visaggio
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  4. Thomas H. Austin
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  5. Mark Stamp
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Correspondence to Mark Stamp.

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Damodaran, A., Troia, F.D., Visaggio, C.A. et al. A comparison of static, dynamic, and hybrid analysis for malware detection. J Comput Virol Hack Tech 13, 1–12 (2017). https://doi.org/10.1007/s11416-015-0261-z

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