Reference : A Trusted Hybrid Learning Approach to Secure Edge Computing
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
A Trusted Hybrid Learning Approach to Secure Edge Computing
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 mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
IEEE Consumer Electronics Magazine
Institute of Electrical and Electronics Engineers
[en] 5G ; Security ; Edge Computing ; Intrustion Detection
[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.
Researchers ; Professionals ; Students ; General public
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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