Gebrezgiher Yonas Teweldemedhin, Jeremiah Sekione Reward, Deng Xianjun, Park Jong Hyuk
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel). 2025 Aug 4;25(15):4793. doi: 10.3390/s25154793.
Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats.
车联网（V2X）通信是智能交通系统发展中的一项基础技术，涵盖车对车（V2V）、车对基础设施（V2I）和车对行人（V2P）通信。这项技术使联网自动驾驶车辆（CAV）能够与周围环境进行交互，显著提高道路安全性、交通效率和驾驶舒适性。然而，随着V2X通信越来越普及，它成为了对抗性和持续性网络攻击的主要目标，对CAV的安全和隐私构成了重大威胁。车辆网络的动态特性以及对实时数据处理和决策的严格要求，使这些挑战更加复杂。许多研究致力于利用机器学习、区块链和密码学等新技术来保障V2X通信的安全。我们的调查突出了V2X通信面临的安全挑战，并评估了当前基于机器学习和区块链的解决方案，揭示了显著的差距和改进机会。具体而言，我们的调查聚焦于将机器学习、区块链和多接入边缘计算（MEC）集成，以实现V2X网络中的低延迟、稳健且动态的安全性的研究。基于我们的研究结果，我们概述了一个概念框架，该框架将机器学习、区块链和MEC协同起来，以应对一些已识别的安全挑战。这个集成框架展示了实时异常检测、去中心化数据共享和增强系统可扩展性的潜力。调查最后确定了未来的研究方向，并概述了面对不断演变的威胁保障V2X通信安全所面临的剩余挑战。
