OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

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Provided by: University of Detroit Mercy
Topic: Security
Format: PDF
Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. In this paper, the authors propose for the first time, OPEM, a hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). They show that this hybrid approach enhances the performance of both approaches when run separately.
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