Reference : Advanced Detection Tool for PDF Threats

	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/10993/13062

Title :	Advanced Detection Tool for PDF Threats
Language :	English
Author, co-author :	Jerome, Quentin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Marchal, Samuel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	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 :	13-Sep-2013
Main document title :	Proceedings of the sixth International Workshop on Autonomous and Spontaneous Security, RHUL, Egham, U.K., 12th-13th September 2013
Publisher :	Springer
Peer reviewed :	Yes
Audience :	International
Event name :	The sixth International Workshop on Autonomous and Spontaneous Security - SETOP
Event date :	12th-13th September 2013
Event organizer :	Telecom Bretagne
Event place (city) :	RHUL, Egham
Event country :	United Kingdom
Keywords :	[en] PDF files ; malware detection ; machine learning
Abstract :	[en] In this paper we introduce an efficient application for malicious PDF detection: ADEPT. With targeted attacks rising over the recent past, exploring a new detection and mitigation paradigm becomes mandatory. The use of malicious PDF files that exploit vulnerabilities in well-known PDF readers has become a popular vector for targeted at- tacks, for which few efficient approaches exist. Although simple in theory, parsing followed by analysis of such files is resource-intensive and may even be impossible due to several obfuscation and reader-specific artifacts. Our paper describes a new approach for detecting such malicious payloads that leverages machine learning techniques and an efficient feature selection mechanism for rapidly detecting anomalies. We assess our approach on a large selection of malicious files and report the experimental performance results for the developed prototype.
Research centres :	Interdisciplinary Center for Security, Reliability and Trust
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/13062

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