Utilizing Generative Adversarial Networks for Secure Communication in Software-Defined Vehicles

The increasing complexity of software-defined vehicles (SDVs) necessitates robust and secure communication protocols to protect against cyber threats. This paper explores the utilization of Generative Adversarial Networks (GANs) to enhance the security of communication protocols in SDVs. GANs, consisting of a generator and a discriminator network, are employed to create and evaluate secure communication sequences, ensuring that unauthorized access and potential attacks are effectively mitigated. In this study, we develop a GAN-based framework that generates secure communication protocols tailored for the dynamic environment of SDVs. The generator is trained to produce communication sequences that are indistinguishable from authentic, secure sequences, while the discriminator is tasked with identifying any anomalies or potential vulnerabilities. By iteratively improving both networks, the framework learns to produce highly secure and resilient communication protocols. The performance of the proposed GAN-based method is evaluated through a series of simulations, demonstrating a significant reduction in successful cyber-attacks compared to traditional communication protocols. The results indicate that our approach enhances security by 35% in terms of attack detection and mitigation, and reduces communication latency by 20%, ensuring not only secure but also efficient communication within SDVs. This study paves the way for integrating AI-driven models like GANs into the development of next-generation secure communication protocols for software-defined vehicles . Keywords: Generative Adversarial Networks, secure communication, software-defined vehicles, cyber threats, AI-driven models, protocol security, attack detection, communication efficiency, SDVs, machine learning