Metamorphic malware detection using minimal opcode statistical patterns

Peyman Khodamoradi

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationSecurity and Privacy Management, Techniques, and Protocols
PublisherIGI Global Publishing
Number of pages23
ISBN (Electronic)9781522555841
ISBN (Print)1522555838, 9781522555834
Publication statusPublished - 6 Apr 2018
Externally publishedYes


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