Adversarial Threats in ML-Driven Wireless Networks: Challenges, Defenses, and the Road Ahead
November 13 @ 12:00 pm - 1:00 pm
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 <a href="http://Professionals.
Register” target=”_blank” title=”Professionals.
Register”>Professionals.
Register now and stay tuned for updates on upcoming speakers and topics!
Speaker(s): Prof. Georges Kaddoum
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 <a href="http://operations.
This” target=”_blank” title=”operations.
This”>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 <a href="http://frameworks.
The” target=”_blank” title=”frameworks.
The”>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 <a href="http://security.
Virtual:” target=”_blank” title=”security.
Virtual:”>security.