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Review

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

1
School of Computing, Robert Gordon University, Aberdeen AB10 7QB, UK
2
School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Rui Pedro Lopes
Electronics 2021, 10(13), 1606; https://doi.org/10.3390/electronics10131606
Received: 29 May 2021 / Revised: 22 June 2021 / Accepted: 29 June 2021 / Published: 5 July 2021
(This article belongs to the Special Issue High Accuracy Detection of Mobile Malware Using Machine Learning)
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. View Full-Text
Keywords: Android security; malware detection; code vulnerability; machine learning Android security; malware detection; code vulnerability; machine learning
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MDPI and ACS Style

Senanayake, J.; Kalutarage, H.; Al-Kadri, M.O. Android Mobile Malware Detection Using Machine Learning: A Systematic Review. Electronics 2021, 10, 1606. https://doi.org/10.3390/electronics10131606

AMA Style

Senanayake J, Kalutarage H, Al-Kadri MO. Android Mobile Malware Detection Using Machine Learning: A Systematic Review. Electronics. 2021; 10(13):1606. https://doi.org/10.3390/electronics10131606

Chicago/Turabian Style

Senanayake, Janaka, Harsha Kalutarage, and Mhd O. Al-Kadri 2021. "Android Mobile Malware Detection Using Machine Learning: A Systematic Review" Electronics 10, no. 13: 1606. https://doi.org/10.3390/electronics10131606

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