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

作     者：Cui, Yingjie Zhou, Hange Zhang, Yongqi Xue, Feng Zheng, Jiabin Lu, Zhenyu Chen, Yong Li, Chuanhai Jiang, Zhonglin Ma, Hui 

作者机构：Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210000 Jiangsu Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511453 Guangdong Peoples R China Ningbo Geely Automobile Res & Dev Co Ltd Ningbo 315311 Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310058 Zhejiang Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100000 Peoples R China Ocean Univ China Sch Informat Sci & Engn Qingdao 266003 Shandong Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：12453-12467页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China (Key Program) [U20B2061] Ningbo Science and Technology Plan Project [2023J188] 

主　　题：Security Routing Vehicle-to-everything Vehicle dynamics Routing protocols Linear programming Real-time systems Network topology Maintenance engineering Wireless sensor networks Communication security hybrid defense intelligent attack Internet of Vehicles (IoV) routing protocol for low-power and lossy network (RPL) vehicle-to-everything (V2X) network 

摘      要：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.