Adversarial Threats in ML-Driven Wireless Networks: Challenges, Defenses, and the Road Ahead

#signalprocessing#communications #6G #machine-learning #network-security #chapters
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Next: "Machine Learning for Resilient 6G Communications: From RF Security to Edge Intelligence" by Prof. Burak Kantarci, University of Ottawa, Canada

Join us for the sixth session of the exciting webinar series on "New Frontiers in Signal Processing in 6G Wireless Networks", a collaboration between IEEE Signal Processing, IEEE Communications Society chapters in Ottawa, and IEEE ComSoC Young Professionals. 

Register now and stay tuned for updates on upcoming speakers and topics!

 


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  • Starts 08 November 2025 01:00 PM UTC
  • Ends 13 November 2025 05:00 PM UTC
  • No Admission Charge

  Speakers

Prof. Georges Kaddoum

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Prof. Georges Kaddoum

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Prof. Georges Kaddoum is a professor and the research director of the Resilient Machine Learning Institute(ReMI) at École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, Canada. He also holds an industrial research chair and a Tier 2 Canada Research Chair.

He earned his Ph.D. in Signal Processing and Telecommunications with High Honors from the National Institute of Applied Sciences (INSA), University of Toulouse, France, in 2009. His research focuses on wireless communication networks, tactical communications, resource allocation, and network security.

Prof. Kaddoum is a member of the Royal Society of Canada and has received multiple prestigious recognitions. His awards include several Best Paper Awards, the Université du Québec Research Excellence Award (2018), the ÉTS Research Excellence Award (2019, 2025), the IEEE TCSC Award for Excellence (2022), the MITACS Award for Exceptional Leadership (2023), and the ADRIQ Partnership Award (2024).

He has served as an associate editor for IEEE Transactions on Information Forensics and Security and IEEE Communications Letters. Currently, he is an area editor for IEEE Transactions on Machine Learning in Communications and Networking and an editor for IEEE Transactions on Communications.

Address:ETS, Montreal, , Canada




Agenda

 

The integration of machine learning (ML) across different layers of modern wireless communication networks has significantly enhanced efficiency, adaptability, and automation. However, thisadvancement has also introduced new security vulnerabilities. Adversarial attacks, in particular, exploit weaknesses in ML models to misclassify signals, degrade network performance, and disrupt critical operations.

This talk examines the growing threat of adversarial attacks that target ML-based signal processing, resource allocation, and security protocols in wireless networks. It discusses the main challenges in protecting ML-driven systems, reviews recent progress in defense techniques such as Bayesian learning, robust training, and adaptive protection methods, and outlines open research directions for developing more resilient and trustworthy intelligent communication frameworks.

The presentation provides an overview of the adversarial landscape, effective defense strategies, and future research opportunities at the intersection of machine learning and wireless network security.


As part of webinar series on "New Frontiers in Signal Processing for 6G Wireless Networks"