Article (Périodiques scientifiques)
A Trusted Hybrid Learning Approach to Secure Edge Computing
Sedjelmaci, Hichem; Senouci, Sidi Mohammed; Ansari, Nirwan et al.
2021In IEEE Consumer Electronics Magazine
Peer reviewed


Texte intégral
Postprint Auteur (472.71 kB)

Tous les documents dans ORBilu sont protégés par une licence d'utilisation.

Envoyer vers


Mots-clés :
5G; Security; Edge Computing; Intrustion Detection
Résumé :
[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.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
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)
Co-auteurs externes :
Langue du document :
Titre :
A Trusted Hybrid Learning Approach to Secure Edge Computing
Date de publication/diffusion :
Titre du périodique :
IEEE Consumer Electronics Magazine
Maison d'édition :
Institute of Electrical and Electronics Engineers
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Projet FnR :
FNR14891397 - Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks, 2020 (01/04/2021-31/03/2024) - Thomas Engel
Disponible sur ORBilu :
depuis le 15 février 2022


Nombre de vues
55 (dont 4 Unilu)
Nombre de téléchargements
179 (dont 5 Unilu)

citations Scopus®
citations Scopus®
sans auto-citations
citations WoS


Publications similaires

Contacter ORBilu