Invited Speakers
Andres Kwasinski
Rochester Institute of Technology, USVisit Homepage
Deep Reinforcement Learning for Distributed And Uncoordinated Cognitive RadiosCognitive radios must autonomously sense the wireless environment and learn to adapt their operation accordingly. Reinforcement Learning (RL) offers a natural framework for realizing this paradigm. Applying the cognitive radio paradigm in its purest form to a spectrum sharing scenario requires radios to operate without exchanging information or coordinating with other devices, either within their own network or across networks operating in the same radio spectrum band. This uncoordinated and distributed setting introduces a key challenge for RL: the environment becomes non-stationary, leading to potentially non-convergent or suboptimal learning. In this talk, I will discuss an uncoordinated and distributed multi-agent DQL (UDMA-DQL) technique that is able to learn effectively in a non-stationary environment by combining a deep neural network with learning in exploration phases and the use of a Best Reply Process with Inertia. I will also discuss an analytical study showing that, over arbitrarily long time, the technique converges with probability one to equilibrium policies, while under a finite time it achieves significantly faster learning compared to an equivalent table-based Q-learning implementation.
Andres Kwasinski is a Professor and Director of the Ph.D. program in Electrical and Computer Engineering at the Rochester Institute of Technology, Rochester, NY, USA. He has co-authored more than 110 peer-reviewed publications and four books published by Cambridge University Press and Wiley. His research interests include cognitive radios and wireless networks, cross-layer techniques in wireless communications, and smart infrastructures and networking. He currently serves on the Senior Editorial Board of the IEEE Signal Processing Magazine, where he has also been Area Editor and Associate Editor. He has previously served as an Editor for IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters. He received the Diploma in Electrical Engineering from the Buenos Aires Institute of Technology, Argentina, and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Maryland, College Park, USA. Dr. Kwasinski is a Senior Member of the IEEE.
Dipankar Dasgupta
Hill Professor in CybersecurityDirector, Center for Information Assurance
The University of Memphis
Visit Homepage
Generic Large Language Models (GLLMs) are continuously being released with increased size and capabilities, promoting the abilities of these tools as universal problem solvers. While the reliability of GLLMs' responses is questionable in many situations, these are augmented/ retrofitted with external resources for different applications including cybersecurity.
The talk will discuss major security concerns of these pre-trained models: first GLLMs are prone to adversarial manipulation such as model poisoning, reverse engineering and side-channel cyberattacks. Second, the security issues related to LLM-generated codes using open-source libraries/codelets for software development can involve software supply chain attacks. These may result in information disclosure, access to restricted resources, privilege escalation, and complete system takeover.
This talk will also cover the benefits and risks of using GLLMs in cybersecurity, particularly, in malware detection, log analysis, intrusion detection, etc. I will highlight the need for diverse AI approaches (non-LLM-based smaller models) trained with application-specific curated data, fine-tuned for well-tested security functionalities in identifying and mitigating emerging cyber threats including zero-day attacks.
Dr. Dipankar Dasgupta is a Professor of Computer Science at the University of Memphis since January 1997. He has extensively worked on the applications of bio-inspired and machine learning approaches to cyber defense. His groundbreaking works, including digital immunity, negative authentication, cloud insurance model, and auth-spectrum, have earned recognition in Computer World Magazine and other media outlets. He received research funding from different federal agencies including NSF, DARPA, IARPA, NSA, NAVY, ONR, DoD and DHS/FEMA. At the National Cyber Leap Year Summit in 2009, Dr. Dasgupta served as a Co-Chair for the Health-Inspired Network Defense working group (see the report, section 6, starting page 46), the results of which have led to a new research program within the Department of Homeland Security’s Science and Technology. With over 300 publications (including 4 patents), 22000+ citations, and an h-index of 68, Dr. Dasgupta's multidisciplinary research is highly acclaimed. He has received numerous awards, including the 2012 Willard R. Sparks Eminent Faculty Award and the 2014 ACM SIGEVO Impact Award. He also received five best paper awards in different international conferences and has organized Symposia on Computational Intelligence in Cyber Security at IEEE SSCI during 2007-2023. Dr. Dasgupta is an IEEE Fellow, AIIA Fellow and NAI Fellow, an ACM Distinguished Speaker (2015-2020), an IEEE Distinguished Lecturer (2022-2024) and 2024 NSF-Fulbright Distinguished Scholar. He regularly serves as a panelist and keynote speaker and offers tutorials in leading computer science conferences and has given more than 350 invited talks in different universities and industries.
Florin Ciucu
Director of Post Graduate ResearchComputer Science Department
University of Warwick
Visit Homepage
Florin Ciucu is a Professor and Director of Post Graduate Research in the CS department at the University of Warwick. His research interests are in the stochastic modelling of communication networks and the non-asymptotic analysis of stochastic bandits. He co-chaired ACM Sigmetrics 2024 and served on the Technical Program Committees of several other top conferences; currently he is on the Editorial Boards of the Performance Evaluation Journal and IEEE Transactions on Networking. Florin is a recipient of the ACM Sigmetrics 2005 Best Student Paper Award and IFIP Performance 2014 Best Paper Award.
Rie Shigetomi YAMAGUCHI
University of Tokyo, JapanVisit Homepage
Associate Professor, Rie Yamaguchi received master’s degree in mathematics from Tsuda College, now Tsuda University, in 2003, and PhD degree in Information Science and Technology from the University of Tokyo in 2006. She joined Information Security Center, National Institute of Advanced Industrial Science and Technology, AIST, in 2006 and concurrently serve in National Information Security Center at Cabinet Secretariat, now National center of Information Incident readiness and Strategy for Cybersecurity from 2007 to 2011. Since 2013 she joined to the University of Tokyo as a Project Associate Professor and current position since 2024.
.png)
