Opportunistic Gossip Learning-Aided Collaborative Physical Layer Authentication for Internet of Vehicles

Traditional device authentication techniques relying only on a single device for authentication are susceptible to poor performance due to insufficient channel observations. Although these challenges are mitigated by centralized collaborative authentication schemes, such schemes assume that all users desire to learn the same model. They further assume constant collaborator presence, leading to poorly trained models in Internet of Vehicles (IoV) environments with fleeting encounters. Moreover, they are vulnerable to GPS spoofing attack. To overcome these limitations, we propose a Gossip Learning (GL)-based collaborative Physical Layer Authentication (PLA) scheme, where collaborators assist vehicles in authenticating transmitters by distributively training their Long Short-term Memory (LSTM) models using the location and Channel State Information (CSI) of the transmitters. The collaborators also improve their model instances by incorporating the learned experiences of other vehicles they meet opportunistically. Instead of using the claimed location of transmitters during authentication, which may be affected by the GPS spoofing attack, their Received Signal Strength (RSS) and Angle of Arrival (AoA) features are used to estimate their current location. The estimated location is then used to predict their current CSI for authentication. Moreover, we propose a distance-based GPS signal spoofing detection algorithm, which ensures that only collaborators not under GPS spoofing attack are selected for collaborative training. The simulation results from experiments conducted using realistic channel attributes obtained from the Quasi-Deterministic Radio channel Generator (QuaDRiGa) platform demonstrate the effectiveness of our system in IoV scenarios, underscoring its relevance to Intelligent Transportation Systems and its superiority over existing techniques.