The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS), significantly enhancing road safety and vehicular communication. However, the intricate and dynamic nature of VANETs presents formidable challenges, particularly in vehicle-to-infrastructure (V2I) communications. Roadside Units (RSUs), integral components of VANETs, are increasingly susceptible to cyberattacks, such as jamming and distributed denial-of-service (DDoS) attacks. These vulnerabilities pose grave risks to road safety, potentially leading to traffic congestion and vehicle malfunctions. Current approaches often struggle to effectively merge digital twin technology with Artificial Intelligence (AI) models to boost security and sustainability. Our study introduces an innovative cyber-twin framework tailored to enhance the security of RSUs in VANETs. This framework uniquely combines digital twin technology with cutting-edge AI to offer a real-time, dynamic representation of RSUs. This allows for detailed monitoring and efficient detection of threats, significantly strengthening RSU security in VANETs. Moreover, our framework makes a notable contribution to eco-friendly communication by improving the computational efficiency of RSUs, leading to increased energy efficiency and extended hardware durability. Our results show a considerable enhancement in resource management and attack detection, surpassing the performance of existing solutions. In particular, the cyber-twin framework showed a substantial reduction in RSU load and an optimal balance between resource consumption and high attack detection efficiency, with a defined twinning rate range of seventy-six to ninety per cent. These advancements underscore our commitment to developing sustainable, secure, and resilient vehicular communication systems for the future of smart cities.

翻译：车载自组织网络（VANETs）的快速发展开启了智能交通系统（ITS）的变革时代，显著提升了道路安全与车辆通信能力。然而，VANETs高度复杂且动态的特性带来了严峻挑战，尤其体现在车辆与基础设施（V2I）通信中。作为VANETs核心组件的路侧单元（RSUs）日益容易遭受网络攻击，例如干扰攻击和分布式拒绝服务（DDoS）攻击。这些漏洞对道路安全构成严重威胁，可能导致交通拥堵和车辆故障。现有方法常难以有效融合数字孪生技术与人智能（AI）模型以提升安全性与可持续性。本研究提出了一种创新的网络-数字孪生框架，专门用于增强VANETs中RSU的安全性。该框架独特地将数字孪生技术与尖端AI相结合，为RSU提供实时动态表征，从而实现威胁的精细监控与高效检测，显著强化了VANETs中RSU的安全防护。此外，本框架通过提升RSU的计算效率，为环保通信做出了显著贡献，实现了更高的能效比与硬件耐久性。我们的实验结果表明，本框架在资源管理与攻击检测方面性能显著优于现有方案，实现了RSU负载的大幅降低，并在资源消耗与高攻击检测效率之间取得了最优平衡，其定义的孪生率范围为76%至90%。这些进展彰显了我们为未来智慧城市构建可持续、安全且强健的车载通信系统的坚定承诺。