Machine learning aided android malware classification

Nikola, M, Dehghantanha, A and Kim-Kwang Raymond, C 2017, 'Machine learning aided android malware classification' , Computers & Electrical Engineering .

[img] PDF - Accepted Version
Restricted to Repository staff only until 22 February 2018.

Download (271kB) | Request a copy
[img] PDF - Published Version
Restricted to Repository staff only

Download (472kB) | Request a copy


The widespread adoption of Android devices and their capability to store ac- cess signi�cant private and con�dential information have resulted in these de- vices 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 anal- ysis of Android malware. The �rst 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 im- plemented 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 classi�cation and permission-based classi�cation 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: 14 Dec 2017 06:35

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year