Machine Learning for Reliable Network Attack Detection in SCADA Systems

Reference : Machine Learning for Reliable Network Attack Detection in SCADA Systems


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/35719

Title :	Machine Learning for Reliable Network Attack Detection in SCADA Systems
Language :	English
Author, co-author :	Lopez Perez, Rocio []
	Adamsky, Florian [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Soua, Ridha [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 :	2018
Main document title :	17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18)
Peer reviewed :	Yes
Audience :	International
Event name :	17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom 18)
Event date :	08-2018
Event place (city) :	New York
Event country :	USA
Keywords :	[en] SCADA ; Anomaly detection ; Machine learning
Permalink :	http://hdl.handle.net/10993/35719
Framework Programme / European Project :	H2020 ; 700581 - ATENA - Advanced Tools to assEss and mitigate the criticality of ICT compoNents and their dependencies over Critical InfrAstructures

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