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IJIET 2016 Vol.6(4): 291-295 ISSN: 2010-3689
DOI: 10.7763/IJIET.2016.V6.702

A Behavior-Based Malware Variant Classification Technique

Guanghui Liang, Jianmin Pang, and Chao Dai

Abstract—The research on detection malware variants attracts much attention in recent years. However current variant classification methods either are interfered by some confusion technologies or have a high time or space complexity. In this paper, a classification technique using dynamic analysis based on behavior profile is proposed. We capture API calls and other essential information of running malware, then establish their multilayer dependency chain according to the dependency relationship of these function calls. In order to deal with the confusion, we remove sequence confusion, sequence noise, and other confusions to optimize the multilayer dependency chain. Finally, a similarity comparison algorithm is used to identify the degree of similarity between malware variants. The experimental results demonstrate that our classification technique is feasible and effective.

Index Terms—Malware, variants, dependency chain.

The authors are with the State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China (e-mail: lghray1987@163.com).


Cite: Guanghui Liang, Jianmin Pang, and Chao Dai, "A Behavior-Based Malware Variant Classification Technique," International Journal of Information and Education Technology vol. 6, no. 4, pp. 291-295, 2016.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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