• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Intelligent Approach for Android Malware Detection

Vol. 9, No.8, August 31, 2015
10.3837/tiis.2015.08.012, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
Shubair Abdulla and Altyeb Altaher, "Intelligent Approach for Android Malware Detection," KSII Transactions on Internet and Information Systems, vol. 9, no. 8, pp. 2964-2983, 2015. DOI: 10.3837/tiis.2015.08.012

[ACM Style]
Abdulla, S. and Altaher, A. 2015. Intelligent Approach for Android Malware Detection. KSII Transactions on Internet and Information Systems, 9, 8, (2015), 2964-2983. DOI: 10.3837/tiis.2015.08.012