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Android malware detection with contrasting permission patterns
journal contribution
posted on 2014-08-01, 00:00 authored by P Xiong, X Wang, W Niu, Tianqing Zhu, Gang LiGang LiAs 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.
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Journal
China communicationsVolume
11Issue
8Pagination
1 - 14Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1673-5447Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, IEEEUsage metrics
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