An Iterative Intelligent Attack With Integrated Strategy for Resource-Constrained Internet of Vehicles

The integration of vehicle-to-everything (V2X) and Internet of Vehicles (IoV) technologies is reshaping vehicular communication, necessitating robust protocols capable of supporting low-power and lossy networks (LLNs). The routing protocol for low-power and lossy networks (RPLs) plays a vital role in enhancing data exchange efficiency and reliability within dynamic vehicular environments. However, RPL faces significant security challenges when adapted to vehicular networks, which differ markedly from the less hostile environments originally envisioned for RPL. In this article, we analyze the security vulnerabilities of RPL in the complex interactive environment of vehicular networks. Specifically, we introduce a novel deep learning-based dynamic attack algorithm to expose the limitations of existing defense mechanisms and highlight RPL’s security vulnerabilities in IoV applications. The experimental results reveal that the proposed attack outperforms traditional attacks in multiple metrics, increasing packet loss by 43%, end-to-end delay by 300%, and reducing link quality throughput by 60% and vehicle node battery life by 50%. Even with in place defense mechanisms, our attack demonstrates greater effectiveness 70% over traditional methods, including version number attacks, wormhole attacks, and DIS flooding attacks. These findings underscore the insufficiency of current security measures and confirm the urgent need for advanced protective strategies to safeguard RPL deployments, particularly in complex IoV communication scenarios.