Mobile malware attacks: Review, taxonomy & future directions

Attia Qamar, Ahmad Karim*, Victor Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

125 Citations (Scopus)


A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent years. Android's popular and attractive environment not only captured the attention of users but also increased security concerns. As a result, Android malware detection is one of the sizzling topics in the mobile security domain. This paper provides a comprehensive review of state-of-the-art mobile malware attacks, vulnerabilities, detection techniques and security solutions over the period of 2013–2019 that majorly targeted Android platform. We have presented various well-organized and in-depth taxonomies that uncover mobile malware detection approaches based on their analysis techniques, working platform, data acquisition, operational impact, obtained results and artificial intelligence component involved. Another taxonomy comprises of mobile malware attack vector is presented to look threat clusters and loopholes to locate their malicious widespread impact on communities. Furthermore, we have discussed and classified forensic analysis efforts in mobile malware detection perspective. From the intruder point of view, we have compared various evasion techniques that are used prominently by the malware authors to hinder detection efforts. Finally, future work directions are presented as guidelines for academia and industry alike to help them reduce or even avoid the harmful impact of these annoying efforts.

Original languageEnglish
Pages (from-to)887-909
Number of pages23
JournalFuture Generation Computer Systems
Publication statusPublished - Aug 2019


  • Android malware
  • Forensics
  • Machine learning
  • Malware
  • Mobile malware
  • Mobile malware detection


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