A review of detecting malware in android devices based on machine learning techniques

Malware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in-depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in-depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection.

Automated summary

This article examines the detection methods of malware on Android mobile devices and provides an in-depth review of previous experiments using machine learning. It thoroughly analyzes the origins, evolution, and sustainability of Android malware detection, and suggests possible research paths.