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

2025-01-8135

04/01/2025

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-8135
Pages
10
Citation
Namburi, V., "Utilizing Generative Adversarial Networks for Secure Communication in Software-Defined Vehicles," SAE Technical Paper 2025-01-8135, 2025, https://doi.org/10.4271/2025-01-8135.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8135
Content Type
Technical Paper
Language
English