An In-Depth Review of Machine Learning Based Android Malware Detection

Ali Muzaffar, Hani Ragab Hassan, Michael Adam Lones, Hind Zantout

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number102833
JournalComputers and Security
Early online date16 Jul 2022
Publication statusE-pub ahead of print - 16 Jul 2022


  • Android security
  • Dynamic malware analysis
  • Machine learning
  • Malware detection
  • Static malware analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Law


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