Applications of deep learning for mobile malware detection: A systematic literature review

Cagatay Catal*, Görkem Giray, Bedir Tekinerdogan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)


For detecting and resolving the various types of malware, novel techniques are proposed, among which deep learning algorithms play a crucial role. Although there has been a lot of research on the development of DL-based mobile malware detection approaches, they were not reviewed in detail yet. This paper aims to identify, assess, and synthesize the reported articles related to the application of DL techniques for mobile malware detection. A Systematic Literature Review is performed in which we selected 40 journal articles for in-depth analysis. This SLR presents and categorizes these articles based on machine learning categories, data sources, DL algorithms, evaluation parameters & approaches, feature selection techniques, datasets, and DL implementation platforms. The study also highlights the challenges, proposed solutions, and future research directions on the use of DL in mobile malware detection. This study showed that Convolutional Neural Networks and Deep Neural Networks algorithms are the most used DL algorithms. API calls, Permissions, and System Calls are the most dominant features utilized. Keras and Tensorflow are the most popular platforms. Drebin and VirusShare are the most widely used datasets. Supervised learning and static features are the most preferred machine learning and data source categories.

Original languageEnglish
Pages (from-to)1007-1032
Number of pages26
JournalNeural Computing and Applications
Issue number2
Early online date23 Oct 2021
Publication statusPublished - 2022


  • Cybersecurity
  • Deep learning
  • Machine learning
  • Malware detection
  • Mobile applications
  • Systematic literature review (SLR)


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