  Malware for Android is becoming increasingly dangerous to the safety of
mobile devices and the data they hold. Although machine learning(ML) 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 us ing 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. Addi tionally, 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 atten tion 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.
