Publication:
Benibo et al., "Q-Learning-Based Adaptive Defense Mechanism for Connected Autonomous Vehicles," SoutheastCon 2025, Concord, NC, USA, 2025, pp. 1063-1064, doi: 10.1109/SoutheastCon56624.2025.10971541. [Slides]
Posters:
Mukwaya et al., "Vulnerability Testing Framework for Connected and Autonomous Vehicles (CAV) Software ", Business, Environment, Communications and Transportation (BECT), Feb 28, 2025. [PDF]
Benibo et al, "Code Diversity-based Defense Mechanism for Connected and Autonomous Vehicles", Business, Environment, Communications, and Transportation (BECT) Symposium, Feb 28, 2025, SCSU. [PDF]
Presentations:
Ruganuza et al., "A Review of Cybersecurity in Connected and Autonomous Vehicles: State of the Art on Code Diversification and Future Research Directions", Business, Environment, Communications and Transportation (BECT) Symposium, Feb 28, 2025. [PDF]
GitHub Repository (Reinforcement Learning for Optimal Code Diversification)
https://github.com/Izison-benny/DDQN-CAV-Cybersecurity-DefenseÂ