GenAI-Driven Cyberattack Detection in V2X Networks for Enhanced Road Safety and Autonomous Vehicle Defense
Keywords:
GenAI, V2X communication, Cyberattack detection, Autonomous vehicles, Road safetyAbstract
In our study, we propose a GenAI-enhanced attack detection framework aimed at improving road safety and cyber defense within vehicle-to-everything (V2X) communication networks. The framework utilizes Generative AI (GenAI) to simulate cyberattacks, such as false data injection (FDI), replay, and stealthy attacks, targeting critical V2X components like On-Board Units (OBUs) and Road-Side Units (RSUs). To detect these threats, we developed an advanced recognition model (Model B), integrating Convolutional Neural Networks (CNN) for spatial data analysis and Long Short-Term Memory (LSTM) networks for temporal data processing. Simulations were conducted in a realistic urban environment using NS-3 and SUMO, testing various V2X communication modes, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) interactions. The experimental results demonstrated that Model B achieved superior performance, with an overall attack detection accuracy of 97%, outperforming conventional methods such as CNN, LSTM, EKF, and OCSVM. Additionally, the system significantly reduced latency in attack detection, particularly in urban traffic scenarios. Our framework was especially effective in identifying replay and stealthy attacks on GPS and LiDAR systems, ensuring minimal disruption to vehicle operations. Our study underscores the importance of utilizing GenAI to enhance attack detection capabilities in V2X networks, contributing to the safety and resilience of autonomous and connected vehicle ecosystems. Future work will focus on optimizing detection algorithms for more complex traffic scenarios and integrating advanced communication technologies such as 6G for further improvements in detection speed and reliability.
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