Reference : Automatically Locating Malicious Packages in Piggybacked Android Apps

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/30028

Title :	Automatically Locating Malicious Packages in Piggybacked Android Apps
Language :	English
Author, co-author :	Li, Li [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Li, Daoyuan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Cai, Haipeng [Washington State University]
	Lo, David [Singapore Management University]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	May-2017
Journal title :	Abstract book of the 4th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft 2017)
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	The 4th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft 2017)
Event date :	from 22-05-2017 to 23-05-2017
Abstract :	[en] To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.
Research centres :	SnT
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/30028

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