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Android malware detection with contrasting permission patterns

Xiong,P, Wang,X, Niu,W, Zhu,T and Li,G 2014, Android malware detection with contrasting permission patterns, China communications, vol. 11, no. 8, pp. 1-14, doi: 10.1109/CC.2014.6911083.

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Title Android malware detection with contrasting permission patterns
Author(s) Xiong,P
Wang,X
Niu,W
Zhu,TORCID iD for Zhu,T orcid.org/0000-0003-3411-7947
Li,GORCID iD for Li,G orcid.org/0000-0003-1583-641X
Journal name China communications
Volume number 11
Issue number 8
Start page 1
End page 14
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2014-08
ISSN 1673-5447
Keyword(s) Android
classification
contrast set
malware detection
permission pattern
Science & Technology
Technology
Telecommunications
Summary As the risk of malware is sharply increasing in Android platform, Android malware detection has become an important research topic. Existing works have demonstrated that required permissions of Android applications are valuable for malware analysis, but how to exploit those permission patterns for malware detection remains an open issue. In this paper, we introduce the contrasting permission patterns to characterize the essential differences between malwares and clean applications from the permission aspect. Then a framework based on contrasting permission patterns is presented for Android malware detection. According to the proposed framework, an ensemble classifier, Enclamald, is further developed to detect whether an application is potentially malicious. Every contrasting permission pattern is acting as a weak classifier in Enclamald, and the weighted predictions of involved weak classifiers are aggregated to the final result. Experiments on real-world applications validate that the proposed Enclamald classifier outperforms commonly used classifiers for Android Malware Detection.
Language eng
DOI 10.1109/CC.2014.6911083
Field of Research 080109 Pattern Recognition and Data Mining
080303 Computer System Security
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071771

Document type: Journal Article
Collection: School of Information Technology
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