Time-efficient detection of false position attack in 5G and beyond vehicular networks

Abstract

5G-V2X-enabled transportation systems rely on seamless cooperation between vehicles, infrastructure, and pedestrians, facilitated by the exchange of real-time position and state information among these entities. Misbehaving vehicles try injecting bogus messages into the network, thereby compromising its reliability and security. This work proposes an efficient Deep Learning (DL)-based Misbehavior Detection System (MDS) that leverages the use of Recurrent Neural Networks (RNNs) to analyze message consistency and detect bogus information in 5G-V2X networks. We highlight the significance of incorporating historical data to analyze consistency. Additionally, we emphasize the importance of evaluating the computational overhead induced by MDSs in V2X networks, given the critical need for low latency communication. We validate our work through both experimental and theoretical studies and compare it to existing works. The obtained results show the effectiveness of our work, achieving an accuracy of 95% in detecting false information injection attacks. Additionally, we guarantee a low required time/computing complexity of O(1), thereby avoiding significant overhead impacting the end-to-end latency when exchanging CAMs.