Research on Intelligent Connected Vehicle Communication Encryption System Based on Machine Learning

This paper proposes an intelligent connected vehicle communication encryption system based on machine learning. By combining RL with an adaptive encryption key generation algorithm, the parameters and key length of the encryption algorithm are dynamically adjusted to adapt to the changing communication environment and security requirements. The reinforcement learning algorithm can be optimized automatically based on the real time communication situation and the potential security threat. Moreover, the adaptive key generation method can increase the computation efficiency of the encryption system. The encryption system is designed and realized, and the experiment is carried out under the condition of simulation vehicle communication. The experimental results show that compared with traditional encryption methods, the system improves encryption efficiency by 20%, reduces computational latency by 15%, and exhibits significant anti-attack capabilities in the face of common denial of service attacks and data tampering attacks. In addition, when dynamically adjusting the encryption strategy, the system can effectively balance encryption strength and computing resources to ensure that system performance is optimized while ensuring security.