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AuthorCatal, Cagatay
AuthorGiray, Görkem
AuthorTekinerdogan, Bedir
Available date2022-11-30T11:23:18Z
Publication Date2022
Publication NameNeural Computing and Applications
ResourceScopus
Resource2-s2.0-85117726634
URIhttp://dx.doi.org/10.1007/s00521-021-06597-0
URIhttp://hdl.handle.net/10576/36776
AbstractFor 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. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectCybersecurity; Deep learning; Machine learning; Malware detection; Mobile applications; Systematic literature review (SLR)
TitleApplications of deep learning for mobile malware detection: A systematic literature review
TypeArticle Review
Pagination1007-1032
Issue Number2
Volume Number34
dc.accessType Open Access


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