Mining permission patterns for contrasting clean and malicious android applications

Moonsamy, V; Rong, J; Liu, S

Mining permission patterns for contrasting clean and malicious android applications

Moonsamy, V Rong, J Liu, S

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2014, 36 pp. 122 - 132
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Moonsamy, V	en_US
dc.contributor.author	Rong, J	en_US
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2014, 36 pp. 122 - 132	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/104844
dc.description.abstract	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.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2013.09.014	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Mining permission patterns for contrasting clean and malicious android applications	en_US
dc.type	Journal Article
utslib.citation.volume	36	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

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.

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http://hdl.handle.net/10453/104844