A Scalable Malware Classification based on Integrated Static and Dynamic Features
Bounouh, Tewfik, Brahimi, Zakaria, Al-Nemrat, A. and Benzaid, Chafika 2017. A Scalable Malware Classification based on Integrated Static and Dynamic Features. in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad (ed.) Global Security, Safety and Sustainability - The Security Challenges of the Connected World Springer International Publishing.
|Authors||Bounouh, Tewfik, Brahimi, Zakaria, Al-Nemrat, A. and Benzaid, Chafika|
|Editors||Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad|
This paper presents a malware classification approach which aims to improve precision and support scalability. To this end, a hybrid approach combining both static and dynamic features is adopted. The hybrid approach has the advantage of being a complete and robust solution to evasion techniques used by malware writers.
The proposed methodology allowed achieving a very promising accuracy of 99.41% in classifying malware into families while considerably reducing the feature space compared to competing approaches in the literature.
|Keywords||Malware classification; Static features; Dynamic features; Coarse-grained modeling|
|Book title||Global Security, Safety and Sustainability - The Security Challenges of the Connected World|
|Publisher||Springer International Publishing|
|04 Jan 2017|
|Publication process dates|
|Deposited||27 Feb 2017|
|Series||Communications in Computer and Information Science|
|Event||11th International Conference on Global Security, Safety, and Sustainability (ICGS3) 2017|
|Digital Object Identifier (DOI)||doi:10.1007/978-3-319-51064-4_10|
The final authenticated publication is available online at https://doi.org/10.1007/978-3-319-51064-4_10
Series Print ISSN 1865-0929 and Online ISSN1865-0937.
|Journal citation||630 (630), pp. 113-124|
|Accepted author manuscript|
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