Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Senanayake, Janaka and Kalutarage, Harsha and Al-Kadri, M. Omar (2021) Android Mobile Malware Detection Using Machine Learning: A Systematic Review. Electronics, 10 (13). p. 1606. ISSN 2079-9292

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Abstract

With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

Item Type: Article
Identification Number: https://doi.org/10.3390/electronics10131606
Dates:
DateEvent
29 June 2021Accepted
5 July 2021Published
Uncontrolled Keywords: Android security; malware detection; code vulnerability; machine learning
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH11 - computing > CAH11-01 - computing > CAH11-01-08 - others in computing
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Networks and Cyber Security
Depositing User: Omar Alkadri
Date Deposited: 05 Apr 2022 13:26
Last Modified: 05 Apr 2022 13:26
URI: http://www.open-access.bcu.ac.uk/id/eprint/12230

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