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- Article name
- AUTOMATION OF MALWARE SIMILARITY ANALYSIS
- Authors
- Lebed R. S., , rioslebed@gmail.com, Lomonosov Moscow State University, Moscow, Russia
- Keywords
- malware / automation / machine learning / similarity analysis / features extraction
- Year
- 2019 Issue 3 Pages 26 - 33
- Code EDN
- Code DOI
- Abstract
- The article proposes an approach to automating similarity analysis of the malicious software based on machine learning algorithms. The proposed approach allows us to estimate the distance for two or more malware samples in the constructed metric space of feature descriptions of executable files in order to determine the degree of similarity. The article describes an algorithm to determine the presence of one of the types of similarities for a group of files. The use of deep learning lets improve the accuracy of the system, reduce the need for expert work of virus analysts, as well as almost completely eliminate manual feature selection. Suggested in this article approach for malware similarity analysis can help identify an attacker during incident response process, design better signatures.
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