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Accueil > Équipes > R3S > Séminaires R3S > Séminaires R3S 2021

Séminaires R3S présentés par Hayfa Ayadi et Arsenia Chorti, le 29/04/2021 à 10h et 14h

L’équipe R3S du laboratoire Samovar vous invite à deux séminaires présentés par Hayfa Ayadi, ATER à l’université de Cergy Paris et Arsenia Chorti , Associate Professor (MCF HDR) ENSEA


Séminaire présenté par Hayfa Ayadi, (ATER à l’université de Cergy Paris)

Quand : jeudi 29 avril 2021 à 10h00

Pour plus d’information, cf intranet du laboratoire Samovar ou en cliquant ici.


Séminaire présenté par Arsenia Chorti , Associate Professor (MCF HDR) ENSEA

Quand : jeudi 29 avril 2021 à 14h00
Moyen : Webconf via le lien https://webconf.imt.fr/frontend/bad-yvk-kbx-xxe

Titre : “Context Aware Security for 6G Wireless”

Lien vidéo de la présentation :

Abstract :
In sixth generation (6G) systems, sensing and advanced artificial intelligence (AI) will enable context awareness, moving from “connected things” to “connected intelligence”. In turn, context awareness can drive Quality of Security (QoSec) and adaptation of security controls, which is especially pertinent for constrained Internet of things (IoT) wireless systems, ultra-low latencies and massive connectivity. Our proposed roadmap for the incorporation of context awareness in 6G wireless security is articulated around the following ideas : i) harvesting context through sensing ; ii) distilling security relevant context through semantic compression ; iii) context aware risk assessment, through semantic fusion of the distilled context with network and application layer information ; iv) developing security controls that can adapt to context. In this talk, we will touch upon certain instances of these domain areas. With respect to adaptive security schemes, we will discuss how physical layer security (PLS) can offer lightweight solutions under statistical Quality of Service (QoS) delay constraints in the radio access. Next, we will show working examples of lightweight distributed anomaly detection in constrained wireless sensor networks. Finally, results from signal processing and deep learning enabled indoor localization will be briefly outlined, to allow early authentication in multi-factor authentication protocols.

Short Biography :
Arsenia (Ersi) Chorti is an Associate Professor (MCF HDR) at the ENSEA, Head of the Information, Communications and Imaging (ICI) Group of the ETIS Lab UMR 8051 and a Visiting Research Fellow at Princeton and Essex Universities. Her research spans the areas of wireless communications and wireless system security for 5G and 6G, with a particular focus on physical layer security. Current research topics include : context aware security, multi-factor authentication protocols, 5G / 6G and IoT, anomaly detection, machine learning for communications, non-orthogonal multiple access (NOMA), faster than Nyquist signalling. She is a Senior IEEE Member, member of the IEEE INGR on Security and of the IEEE P1951.1 standardization workgroup (Smart Cities), while from March 2021 she is on a half-year sabbatical leave (CNRS delegation) to Princeton University and TU Dresden (postponed to 2022 due to covid-19).