loading page

Android malware Detection using Machine learning: A Review
  • +3
  • Md Naseef-Ur-Rahman Chowdhury ,
  • Ahshanul Haque ,
  • Hamdy Soliman ,
  • Mohammad Sahinur Hossen ,
  • Imtiaz Ahmed ,
  • Tanjim Fatima
Md Naseef-Ur-Rahman Chowdhury
Author Profile
Ahshanul Haque
New Mexico Tech

Corresponding Author:[email protected]

Author Profile
Hamdy Soliman
Author Profile
Mohammad Sahinur Hossen
Author Profile
Imtiaz Ahmed
Author Profile
Tanjim Fatima
Author Profile


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.