A Scalable Malware Classification based on Integrated Static and Dynamic Features

Conference paper


Bounouh, Tewfik, Brahimi, Zakaria, Al-Nemrat, A. and Benzaid, Chafika 2017. A Scalable Malware Classification based on Integrated Static and Dynamic Features. 11th International Conference on Global Security, Safety, and Sustainability (ICGS3) 2017. London, UK 18 - 20 Jan 2017 Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_10
AuthorsBounouh, Tewfik, Brahimi, Zakaria, Al-Nemrat, A. and Benzaid, Chafika
TypeConference paper
Abstract

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.

KeywordsMalware classification; Static features; Dynamic features; Coarse-grained modeling
Year2017
Conference11th International Conference on Global Security, Safety, and Sustainability (ICGS3) 2017
PublisherSpringer International Publishing
Accepted author manuscript
License
CC BY-NC-ND
File Access Level
Anyone
Publication dates
Online04 Jan 2017
Publication process dates
Deposited27 Feb 2017
Journal citation630, pp. 113-124
ISSN1865-0929
Book titleGlobal Security, Safety and Sustainability - The Security Challenges of the Connected World
Book editorJahankhani, H.
Carlile, A.
Emm, D.
Hosseinian-Far, A.
Brown, G.
Sexton, G.
Jamal, A.
ISBN978-3-319-51063-7
978-3-319-51064-4
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-51064-4_10
Web address (URL)https://doi.org/10.1007/978-3-319-51064-4
Copyright holder© 2016 Springer International Publishing AG
Copyright informationThe final authenticated publication is available online at https://doi.org/10.1007/978-3-319-51064-4_10
SeriesCommunications in Computer and Information Science
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Accepted author manuscript
Sclable Malware Classification - Ameer Al-Nemrat.pdf
License: CC BY-NC-ND
File access level: Anyone

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