Android malware Detection using Machine learning: A Review
- Md Naseef-Ur-Rahman Chowdhury ,
- Ahshanul Haque ,
- Hamdy Soliman ,
- Mohammad Sahinur Hossen ,
- Imtiaz Ahmed ,
- Tanjim Fatima
Abstract
Malware for Android is becoming increasingly dangerous to the safety of
mobile devices and the data they hold. Although machine learning
techniques have been shown to be effective at detecting malware for
Android, a comprehensive analysis of the methods used is required. We
review the current state of Android malware detection using machine
learning in this paper. We begin by providing an overview of Android
malware and the security issues it causes. Then, we look at the various
supervised, unsupervised, and deep learning machine learning approaches
that have been utilized for Android malware detection. Additionally, we
present a comparison of the performance of various Android malware
detection methods and talk about the performance evaluation metrics that
are utilized to evaluate their efficacy. Finally, we draw attention to
the drawbacks and difficulties of the methods that are currently in use
and suggest possible future directions for research in this area. In
addition to providing insights into the current state of Android malware
detection using machine learning, our review provides a comprehensive
overview of the subject.