Exploring LSTM-assisted A2C For Physical Layer Security in Vehicular Cyber-Physical Systems

Physical layer security is of paramount importance in vehicular cyber-physical systems, as it safeguards not only the privacy of sensitive data exchanged between vehicles and infrastructure but also ensures the integrity and reliability of the entire transportation network. Key generation plays a crucial role in establishing secure communication channels and facilitating the creation of unique cryptographic keys used for encryption, decryption, and authentication purposes. The secret key generation involves deriving secret bits by harnessing the inherent randomness present within the communication channels. The difficulty lies in precisely evaluating the randomness of the channel to achieve unanimous agreement on secure key generation within an unpredictable environment. In this line, we propose a combinatorial approach involving A2C and LSTM to decrease the key disagreement rate. A2C employs policy and value-based strategies to choose quantization levels predicated on the randomness of wireless channels and LSTM includes the partially observable radio channels and improves the environment. Based on our performance evaluation, the proposed A2C-LSTM method substantially accelerates the convergence rate by $\mathbf{5 0 - 6 0 \%}$ and reduces the Key Disagreement Rate (KDR) by $40 \%$.