AndroAnalyzer: android malicious software detection based on deep learning

ARSLAN R. S.

PEERJ COMPUTER SCIENCE, vol.7, pp.1-20, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 7
  • Publication Date: 2021
  • Doi Number: 10.7717/peerj-cs.533
  • Journal Name: PEERJ COMPUTER SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Directory of Open Access Journals
  • Page Numbers: pp.1-20
  • Kayseri University Affiliated: No

Abstract

Background: Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods.