Machine learning aided android malware classification

Nikola, M, Dehghantanha, A ORCID: 0000-0002-9294-7554 and Kim-Kwang Raymond, C 2017, 'Machine learning aided android malware classification' , Computers & Electrical Engineering, 61 , pp. 266-274.

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Abstract

The widespread adoption of Android devices and their capability to store access significant private and confidential information have resulted in these devices being targeted by malware developers. Existing Android malware analysis techniques can be broadly categorized into static and dynamic analysis. In this paper, we present two machine learning aided approaches for static analysis of Android malware. The first approach is based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model. Our permission-based model is computationally inexpensive, and is implemented as the OWASP Seraphimdroid Android app that can be obtained from Google Play Store. Our evaluations of both approaches indicate an F- score of 95.1% and F-measure of 89% for the source code-based classification and permission-based classification models, respectively.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Computers & Electrical Engineering
Publisher: Elsevier
ISSN: 0045-7906
Related URLs:
Funders: European Research Council
Depositing User: Dr. Ali Dehghantanha
Date Deposited: 13 Mar 2017 08:33
Last Modified: 21 Aug 2018 14:14
URI: http://usir.salford.ac.uk/id/eprint/41554

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