Intelligent approach for android malware detection

Shubair Abdulla*, Altyeb Altaher

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

Original languageEnglish
Pages (from-to)2964-2983
Number of pages20
JournalKSII Transactions on Internet and Information Systems
Volume9
Issue number8
DOIs
Publication statusPublished - Aug 31 2015

Keywords

  • Android
  • Classification
  • Malware detection
  • Neuro-fuzzy
  • System permissions

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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