Machine learning aided android malware classification

Nikola, M; Dehghantanha, A; Kim-Kwang Raymond, C

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.

• Journal Article Type: Article
• Acceptance Date: Feb 13, 2017
• Online Publication Date: Feb 22, 2017
• Publication Date: Feb 22, 2017
• Deposit Date: Mar 13, 2017
• Publicly Available Date: Feb 22, 2018
• Journal: Computers & Electrical Engineering
• Print ISSN: 0045-7906
• Publisher: Elsevier
• Volume: 61
• Pages: 266-274
• DOI: https://doi.org/10.1016/j.compeleceng.2017.02.013
• Publisher URL: http://dx.doi.org/10.1016/j.compeleceng.2017.02.013
• Related Public URLs: https://www.journals.elsevier.com/computers-and-electrical-engineering/