Android mobile malware detection using machine learning: a systematic review.

Senanayake, Janaka; Kalutarage, Harsha; Al-Kadri, Mhd Omar

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.

Citation

SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2021. Android mobile malware detection using machine learning: a systematic review. Electronics [online], 10(13), article 1606. Available from: https://doi.org/10.3390/electronics10131606

• Journal Article Type: Article
• Acceptance Date: Jun 29, 2021
• Online Publication Date: Jul 5, 2021
• Publication Date: Jul 15, 2021
• Deposit Date: Jul 19, 2021
• Publicly Available Date: Jul 19, 2021
• Journal: Electronics
• Electronic ISSN: 2079-9292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 13
• Article Number: 1606
• DOI: https://doi.org/10.3390/electronics10131606
• Keywords: Android security; Malware detection; Code vulnerability; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/1380690