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. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. Our study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs and address this gap. This framework enables real-time monitoring and efficient threat detection while also improving computational efficiency and reducing data transmission delay for increased energy efficiency and hardware durability. Our framework outperforms existing solutions in resource management and attack detection. It reduces RSU load and data transmission delay while achieving an optimal balance between resource consumption and high attack detection effectiveness. This highlights our commitment to secure and sustainable vehicular communication systems for 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载通信能力。然而，VANETs复杂且动态的特性带来了严峻挑战，尤其在车对基础设施（V2I）通信方面。作为VANETs关键组件的路侧单元（RSUs）日益易受网络攻击，如干扰和分布式拒绝服务（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。现有方法在检测动态攻击以及整合数字孪生技术与人工智能（AI）模型以增强VANET网络安全方面面临困难。本研究提出了一种融合数字孪生技术与AI的新型框架，以增强VANETs中RSUs的安全性并填补这一空白。该框架能够实现实时监控和高效威胁检测，同时提升计算效率、降低数据传输延迟，从而提高能效并延长硬件使用寿命。在资源管理和攻击检测方面，本框架优于现有解决方案。它可减轻RSU负载、降低数据传输延迟，并在资源消耗与高攻击检测效能之间实现最佳平衡。这体现了我们致力于为智慧城市构建安全、可持续的车载通信系统的承诺。