GAN-Driven Defense for Securing Autonomous Vehicles from Emerging Cyber Threats
Published in 2026 Global Conference on Wireless and Optical Technologies (GCWOT), 2026
Recommended citation: Mirza Akhi, Md Shalha Mucha Bhuiyan, Enrique Nava, Sanober Farheen Memon, Noorain Mukhtiar, Reenu Mohandas, and Lubna Luxmi Dhirani. (2026). "GAN-Driven Defense for Securing Autonomous Vehicles from Emerging Cyber Threats" GCWOT2026. https://ieeexplore.ieee.org/abstract/document/11499711
This paper presents a Diffusion-GAN-based approach for detecting Distributed Denial of Service (DDoS) attacks in 5G-enabled autonomous vehicle environments. The proposed model combines diffusion-based latent conditioning with residual fusion to improve cyber threat detection in intelligent transportation systems. It achieves high detection accuracy on the UL-ECE-5G-AV-DDoS2025 dataset and integrates GPT-3.5 Turbo with zero-shot prompting for post-detection anomaly analysis and actionable cybersecurity insights. The results demonstrate the potential of combining Generative AI and large language models for adaptive real-time AV cybersecurity.
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Recommended citation:
‘Akhi, Mirza, Md Shalha Mucha Bhuiyan, Enrique Nava, Sanober Farheen Memon, Noorain Mukhtiar, Reenu Mohandas, and Lubna Luxmi Dhirani. (2026). “GAN-Driven Defense for Securing Autonomous Vehicles from Emerging Cyber Threats.” 2026 Global Conference on Wireless and Optical Technologies (GCWOT), 1-6.’