Securing 6G-enabled vehicle-to-everything communications: A blockchain-enabled collaborative intrusion detection framework with reinforcement learning
Massinissa Chelghoum, Gueltoum Bendiab, Mohamed Benmohammed, Mohamed Aymen Labiod, Samia Bousalem, Abdelhamid Mellouk · Computer Networks · 2025
The emergence of 6G technology is set to revolutionize connected autonomous vehicles (CAVs) by enabling hyper-connectivity, ultra-reliable low-latency communication, and seamless integration with IoT systems. These advancements enhance CAV efficiency, intelligence, and real-time data exchange for safer navigation and decision-making. However, the interconnected nature of 6G-enabled CAVs introduces significant cybersecurity risks, including adversarial AI attacks and large-scale intrusions. Traditional security methods are inadequate for addressing the complexity and sophistication of emerging threats in this dynamic ecosystem. This article aims to address these critical challenges by proposing an innovative security framework tailored for 6G-enabled CAVs. By integrating blockchain technology, reinforcement learning, and collaborative intrusion detection systems, this framework aims to secure CAV communications against malicious intrusions and ensure the reliability of their AI-driven operations. The system was evaluated on a dataset containing 2D image representations of normal and malicious network traffic. The ensemble-based IDS demonstrated high detection accuracy (99%) with low false positive rates. The Q-learning agent effectively supported trust-based consensus by reliably selecting validators and isolating malicious nodes. The blockchain layer maintained stable validation and propagation times, confirming the framework’s scalability and low-latency performance.