Detection approaches for android malware: Taxonomy and review analysis

• Published in: Expert systems with applications Vol. 238; p. 122255
• Main Authors: Haidros Rahima Manzil, Hashida, Manohar Naik, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Android malware detection; Behavior based analysis; Malware analysis; Signature based analysis; Behavior based analysis; Android malware detection; Signature based analysis; Malware analysis
• DOI: 10.1016/j.eswa.2023.122255
• ISSN: 0957-4174

The main objective of this review is to present an in-depth study of Android malware detection approaches. This article provides a comprehensive survey of 150 studies on Android malware detection from 2010 to 2022. Two broader categories like traditional signature-based and behavior-based approaches are discussed throughout the review process. The behavior-based detection approaches are further categorized in to static, dynamic, and hybrid analysis methods. The survey has conducted in different dimensions including detection approaches, datasets used, features, sustainability of the solutions, etc. Although researchers have proposed detection tools and techniques to develop efficient countermeasures against Android malware, there is a scarcity of a concise review for research practitioners in this subject area. The survey shows there is a great deal of interest in machine learning-based detection methods among the research community. The review not only provides an authentic assessment of the malware detection capabilities of different approaches but also presents observations and suggestions regarding various aspects of the Android malware ecosystem. These observations and suggestions are intended to assist researchers in enhancing further research towards the subject domain.