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效能。然而，VANETs复杂动态的本质特性带来了严峻挑战，尤其体现在车辆-基础设施（V2I）通信领域。作为VANETs核心组件的路侧单元（RSUs），正日益遭受诸如干扰攻击和分布式拒绝服务（DDoS）攻击等网络威胁。这些脆弱性对道路安全构成重大风险，可能导致交通拥堵与车辆故障。现有方法通常难以有效融合数字孪生技术与人工智能（AI）模型以增强安全性与可持续性。本研究提出一种创新的网络孪生框架，专为增强VANETs中RSUs的安全性而设计。该框架创新性地将数字孪生技术与前沿AI相结合，为RSUs提供实时动态表征，从而实现精细化监控与高效威胁检测，显著强化VANETs中RSUs的安全防护。此外，本框架通过提升RSUs计算效率，在提高能效与延长硬件耐久性方面为环保通信做出重要贡献。实验结果表明，本框架在资源管理与攻击检测方面均取得显著提升，性能超越现有方案。特别地，网络孪生框架实现了RSU负载的实质性降低，并在资源消耗与高攻击检测效率之间达到最优平衡，其孪生速率范围界定为76%至90%。这些进展彰显了本团队为未来智慧城市构建可持续、安全且具弹性的车载通信系统的不懈努力。