Webinars

Fall 2024: 

Webinar (1): Enhancing AI/ML-Based Attack Detection in Connected and Autonomous Vehicles
through Generative AI and Combinatorial Fusion Analysis

Speeker: Dr. Mohamed Rahouti, Fordham University 

Date and Time: October 9, 2024 at 11:00am CST

Abstract

Ensuring the security and reliability of Connected and Autonomous Vehicles (CAVs) necessitates robust intrusion and attack detection mechanisms. While AI and ML methods have shown promise in this area, there is a pressing need for strategies that enhance their generalizability and
robustness. This talk will explore the synergy of Generative AI (GAI) and Combinatorial Fusion Analysis (CFA) in improving attack detection systems for CAVs. CFA integrates multiple pre- trained AI/ML models using sophisticated fusion algorithms, enhancing overall performance and
reliability. Simultaneously, GAI models, such as GANs, VAEs, and GPTs, can augment and balance datasets, generating new features to enrich data representation. The combination of GAI and CFA offers a powerful and sustainable platform for detecting and mitigating a wide range of cyber threats in CAV environments. This presentation will delve into recent advances in intrusion detection, highlighting the effectiveness of the GAI/CFA approach specifically tailored for CAVs.

 

Biography

Mohamed Rahouti received an M.S. degree in Mathematics (Statistics Concentration) and a Ph.D. degree in Electrical and Computer Engineering, both from the University of South Florida (Tampa, FL). He is currently an Assistant Professor in the Department of Computer and Information Science at Fordham University in New York City. His research interest focuses on blockchain technology, computer networking, machine learning, and network security with applications to smart cities. Dr. Rahouti has authored/co-authored over 50 peer-reviewed journals/conference papers and is a member of the IEEE Computer and Communications Societies.