Arsenia Chorti is a Professor at the École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA) at the ETIS Lab UMR 8051, Research Fellow of the Barkhausen Institut gGmbH and a Visiting Scholar at Princeton University. Her research spans the areas of wireless communications and wireless systems 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, new multiple access techniques and scheduling. She is a Senior IEEE Member, Associate Editor in Chief of the IEEE ComSoc Best Readings, Member of the IEEE INGR on Security, Chair of the IEEE Focus Group on Physical Layer Security and a Member of various ITU Working Groups including on Security of the Metaverse. She has participated in the reduction of the ITU report M.2516-0 on Future technology trends of terrestrial International Mobile Telecommunications systems towards 2030 and beyond (sections on trustworthiness) and has served in the IEEE P1940 Standardization Workgroup on Standard profiles for ISO 8583 authentication services and was a member of the IEEE Teaching Awards Committee.

(Onsite Talk) Speech Title: Smart, adaptive, context aware physical layer security for 6G: announcing the first PLS security challenge

Abstract: Smart, adaptive security is envisioned for future, heterogeneous networks of highly diverse KPIs (delays, energy consumption, massive connectivity / scalability, computational power, etc.). In parallel, the integration of communications and sensing along with embedded artificial intelligence can provide the foundations for building autonomous and adaptive security protocols. In this talk, we will shed light on how physical layer security (PLS) is naturally adaptive and will further discuss a roadmap for its incorporation in 6G. To command the confidence of the academic and entrepreneurial communities as well as of the end users, we will present the first ever PLS security challenge for secret key generation (SKG) schemes in indoor environments, mirroring well established practices in the crypto community. Finally, a comprehensive list of countermeasures is presented for the majority of known active attacks on SKG.

Prof. Mingcong Deng, IEEE Fellow
Tokyo University of Agriculture and Technology, Japan

Prof. Mingcong Deng received his PhD in Systems Science from Kumamoto University, Japan, in 1997. From 1997.04 to 2010.09, he was with Kumamoto University; University of Exeter, UK; NTT Communication Science Laboratories; Okayama University. From 2010.10, he has been with Tokyo University of Agriculture and Technology, Japan, as a professor. Prof. Deng specializes in three complementary areas: Learning & operator based nonlinear fault detection and fault tolerant control system design; System design on human factor based robot control; Learning based nonlinear adaptive control. Prof. Deng has over 550 publications including 210 journal papers in peer reviewed journals including IEEE Transactions, IEEE Press and other top tier outlets. He serves as a chief editor for 2 international journals, and associate editors of 6 international journals. Prof. Deng is a co-chair of agricultural robotics and automation technical committee, IEEE Robotics and Automation Society; Also a chair of the environmental sensing, networking, and decision making technical committee, IEEE SMC Society. He was the recipient of 2014 & 2019 Meritorious Services Award of IEEE SMC Society, 2020 IEEE RAS Most Active Technical Committee Award (IEEE RAS Society). He is a fellow of The Engineering Academy of Japan, and a fellow of IEEE

(Onsite Talk) Speech Title: Learning & Operator based Control to Ensure Bounded Signals of Nonlinear Systems with Smart Material Actuators and Sensors
Abstract: Learning based nonlinear control design is necessary to ensure bounded signals of nonlinear systems. Recently, smart materials have been used as actuators and sensors in many nonlinear dynamic systems to realize the reduction in size and weight of the systems, such as piezoelectric elements, shape-memory alloy etc. In this talk, for obtaining bounded system signals, based on operator theory nonlinear control schemes for systems with piezoelectric actuators & sensors is introduced, nonlinear control for a system using an interactive shape memory alloy actuation is also shown. Further, current results are shown to combine learning schemes.