Reference : A Trusted Hybrid Learning Approach to Secure Edge Computing

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/50279

Title :	A Trusted Hybrid Learning Approach to Secure Edge Computing
Language :	English
Author, co-author :	Sedjelmaci, Hichem [Orange Labs]
	Senouci, Sidi Mohammed [Univ. Bourgogne Franche Comté > DRIVE EA1859,]
	Ansari, Nirwan [New Jersey Institute of Technology > Advanced Networking Lab]
	Boualouache, Abdelwahab [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	2021
Journal title :	IEEE Consumer Electronics Magazine
Publisher :	Institute of Electrical and Electronics Engineers
Peer reviewed :	Yes
Audience :	International
e-ISSN :	2162-2256
Keywords :	[en] 5G ; Security ; Edge Computing ; Intrustion Detection
Abstract :	[en] Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks against attackers targeting the connected edge devices or the wireless channel. However, the proposed detection mechanisms could generate a high false detection rate, especially against unknown attacks defined as zero-day threats. Thereby, we propose and conceive a new hybrid learning security framework that combines the expertise of security experts and the strength of machine learning to protect the edge computing network from known and unknown attacks, while minimizing the false detection rate. Moreover, to further decrease the number of false detections, a cyber security mechanism based on a Stackelberg game is used by the hybrid learning security engine (activated at each edge server) to assess the detection decisions provided by the neighboring security engines.
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/50279
DOI :	10.1109/MCE.2021.3099634
FnR project :	FnR ; FNR14891397 > Thomas Engel > 5G-INSIGHT > Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks > 01/02/2021 > > 2020

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