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Mining permission patterns for contrasting clean and malicious android applications

Moonsamy,V, Rong,J and Liu,S 2014, Mining permission patterns for contrasting clean and malicious android applications, Future generation computer systems, vol. 36, pp. 122-132, doi: 10.1016/j.future.2013.09.014.

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Title Mining permission patterns for contrasting clean and malicious android applications
Author(s) Moonsamy,V
Rong,J
Liu,S
Journal name Future generation computer systems
Volume number 36
Start page 122
End page 132
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2014-07
ISSN 0167-739X
1872-7115
Keyword(s) Android permission
Biclustering
Contrast mining
Data mining
Permission pattern
Science & Technology
Technology
Computer Science, Theory & Methods
Computer Science
MALWARE
Summary An Android application uses a permission system to regulate the access to system resources and users' privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved.
Language eng
DOI 10.1016/j.future.2013.09.014
Field of Research 080503 Networking and Communications
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier BV
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071918

Document type: Journal Article
Collection: School of Information Technology
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