| 한글제목(Korean Title) |
Intelligent Approach for Android Malware Detection |
| 영문제목(English Title) |
Intelligent Approach for Android Malware Detection |
| 저자(Author) |
Shubair Abdulla
Altyeb Altaher
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| 원문수록처(Citation) |
VOL 09 NO. 08 PP. 2964 ~ 2983 (2015. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Android
neuro-fuzzy
malware detection
system permissions
classification
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| 파일첨부 |
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