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Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

Mahmood Fazlali, Peyman Khodamoradi
Copyright: © 2018 |Pages: 23
ISBN13: 9781522555834|ISBN10: 1522555838|EISBN13: 9781522555841
DOI: 10.4018/978-1-5225-5583-4.ch014
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MLA

Fazlali, Mahmood, and Peyman Khodamoradi. "Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns." Security and Privacy Management, Techniques, and Protocols, edited by Yassine Maleh, IGI Global, 2018, pp. 337-359. https://doi.org/10.4018/978-1-5225-5583-4.ch014

APA

Fazlali, M. & Khodamoradi, P. (2018). Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns. In Y. Maleh (Ed.), Security and Privacy Management, Techniques, and Protocols (pp. 337-359). IGI Global. https://doi.org/10.4018/978-1-5225-5583-4.ch014

Chicago

Fazlali, Mahmood, and Peyman Khodamoradi. "Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns." In Security and Privacy Management, Techniques, and Protocols, edited by Yassine Maleh, 337-359. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5583-4.ch014

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Abstract

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

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