Reference : Using opcode-sequences to detect malicious Android applications

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/20500

Title :	Using opcode-sequences to detect malicious Android applications
Language :	English
Author, co-author :	Jerome, Quentin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Allix, Kevin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)]
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Publication date :	Jun-2014
Main document title :	IEEE International Conference on Communications, ICC 2014, Sydney Australia, June 10-14, 2014
Publisher :	IEEE
Peer reviewed :	Yes
City :	Sydney
Country :	Australia
Event name :	International Conference on Communications (ICC)
Event date :	From 10-06-2014 to 14-06-2014
Event organizer :	IEEE
Event place (city) :	Sydney
Event country :	Australia
Keywords :	[en] Android Malware ; opcode-sequences ; machine learning
Abstract :	[en] Recently, the Android platform has seen its number of malicious applications increased sharply. Motivated by the easy application submission process and the number of alternative market places for distributing Android applications, rogue authors are developing constantly new malicious programs. While current anti-virus software mainly relies on signature detection, the issue of alternative malware detection has to be addressed. In this paper, we present a feature based detection mechanism relying on opcode-sequences combined with machine learning techniques. We assess our tool on both a reference dataset known as Genome Project as well as on a wider sample of 40,000 applications retrieved from the Google Play Store.
Permalink :	http://hdl.handle.net/10993/20500
DOI :	10.1109/ICC.2014.6883436
Other URL :	http://dx.doi.org/10.1109/ICC.2014.6883436

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