An in-depth review of machine learning based Android malware detection. Issue 121 (October 2022)

• Record Type: Journal Article
• Authors: Muzaffar, Ali; Ragab Hassen, Hani; Lones, Michael A.; Zantout, Hind
• Is Part Of: Computers & security. Issue 121(2022)
• Publication Date: 2022-10
• Subjects: Malware detection -- Android security -- Static malware analysis -- Dynamic malware analysis -- Machine learning; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2022.102833
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: It is estimated that around 70% of mobile phone users have an Android device. Due to this popularity, the Android operating system attracts a lot of malware attacks. The sensitive nature of data present on smartphones means that it is important to protect against these attacks. Classic signature-based detection techniques fall short when they come up against a large number of users and applications. Machine learning, on the other hand, appears to work well, and also helps in identifying zero-day attacks, since it does not require an existing database of malicious signatures. In this paper, we critically review past works that have used machine learning to detect Android malware. The review covers supervised, unsupervised, deep learning and online learning approaches, and organises them according to whether they use static, dynamic or hybrid features.