Milosevic, N., Dehghantanha, A. and Choo, K.-K.R. (2017) Machine learning aided Android malware classification. Computers & Electrical Engineering, 61. pp. 266-274. ISSN 0045-7906
Abstract
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 Elsevier Ltd. This is an author produced version of a paper subsequently published in Computers & Electrical Engineering. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Static malware analysis; OWASP; Seraphimdroid Android app; Machine learning |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Mar 2018 10:48 |
Last Modified: | 12 Mar 2018 13:20 |
Published Version: | https://doi.org/10.1016/j.compeleceng.2017.02.013 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.compeleceng.2017.02.013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128366 |