Android Malware Detection using Machine learning: A Review

TL;DR

This paper reviews the current use of machine learning techniques for Android malware detection, analyzing various approaches, their performance, challenges, and future research directions.

Contribution

It provides a comprehensive overview of machine learning methods for Android malware detection, including a comparison of their performance and discussion of existing challenges.

Findings

Supervised, unsupervised, and deep learning approaches are used for detection.

Performance varies across different methods and metrics.

Current methods face significant drawbacks and challenges.

Abstract

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.