FraudDroid: Automated Ad Fraud Detection for Android Apps

Reference : FraudDroid: Automated Ad Fraud Detection for Android Apps


	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/10993/37891

Title :	FraudDroid: Automated Ad Fraud Detection for Android Apps
Language :	English
Author, co-author :	Dong, Feng [Beijing University of Posts and Telecommunications, China]
	Wang, Haoyu [Beijing University of Posts and Telecommunications, China]
	Li, Li [Monash University]
	Guo, Yao [Peking University]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Liu, Tianming [Beijing University of Posts and Telecommunications, China]
	Xu, Guoai [Beijing University of Posts and Telecommunications, China]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Nov-2018
Main document title :	ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018)
Pages :	257-268
Peer reviewed :	Yes
Audience :	International
Event name :	ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018)
Event date :	Nov. 2018
Event place (city) :	Lake Buena Vista
Event country :	USA
Permalink :	http://hdl.handle.net/10993/37891
FnR project :	FnR ; FNR11693861 > Jacques Klein > CHARACTERIZE > Characterization of Malicious Code in Mobile Apps: Towards Accurate and Explainable Malware Detection > 01/06/2018 > 31/05/2021 > 2017

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Fulltext file(s):

	File	Commentary	Version	Size	Access
Open access	2018-FSE-FraudDroid (1).pdf		Author preprint	1.92 MB	View/Open

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