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- Article name
- Data mining methods for malware detection: review of state-of-the-art
- Authors
- Kotenko I. V., , ivkote@comsec.spb.ru, Institution of the Russian Academy of Sciences St.-Petersburg Institute for Informatics and Automation RAS, Russia
Komashinskiy D. V., , komashinskiy@comsec.spb.ru, St.-Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St.-Petersburg, Russia
- Keywords
- mining / malware / detection / survey
- Year
- 2013 Issue 4 Pages 21 - 33
- Code EDN
- Code DOI
- Abstract
- The paper focuses on the common design process of systems whose aims are to detect and identify malware on the base of data mining methods. The generalized models of learning and functioning processes are formed on the base of existing approaches discussed in papers devoted to the topic. The set of basic abstract items specifying the essence of each concrete approach to detect malware is emphasized. Then the survey is focused on the existing approaches in order to uncover their details touching the following abstract items: used types of features, data sets, feature selection approaches, learning methods and data mining software.
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