Machine learning aided Android malware classification

Nikola Milošević

doi:10.1016/j.compeleceng.2017.02.013

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Machine learning aided Android malware classification

2017-07-01

184

Nikola Milošević

Ali Dehghantanha

Kim‐Kwang Raymond Choo *

DOI：10.1016/j.compeleceng.2017.02.013

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Abstract

En 中文

The widespread adoption of Android devices and their capability to 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 feature of 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. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords:

Static malware analysis

OWASP

Seraphimdroid Android app

OWASP Seraphimdroid Android app

Machine learning

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