The rapid development and integration of intelligent technologies in the Internet of Vehicles (IoV) have revolutionized transportation systems by enhancing connectivity, automation, and safety. However, the complexity and connectivity of IoV networks also introduce security challenges, including data privacy concerns, cyber threats, and system vulnerabilities. This paper surveys the role of Edge Computing (EC), Machine Learning (ML), and Deep Learning (DL) in strengthening IoV security frameworks. It examines the synergy between these technologies, highlighting their individual capabilities and their collective impact on enhancing threat detection, response times, and adaptive security. Through real world case studies and practical deployments, we demonstrate how EC, ML, and DL are currently improving security and operational efficiency in IoV systems. The paper also identifies key research gaps and future directions for further advancements in IoV security, including the need for scalable, privacy preserving solutions and robust defense mechanisms against emerging cyber threats. By integrating EC, ML, and DL, this work lays the groundwork for developing adaptive, efficient, and resilient IoV security infrastructures capable of addressing evolving challenges in the transportation ecosystem.

翻译：车联网（IoV）中智能技术的快速进步与整合通过增强连接性、自动化及安全性彻底变革了交通系统。然而，IoV网络的复杂性与互联性也带来了安全挑战，包括数据隐私问题、网络威胁及系统漏洞。本文系统综述了边缘计算（Edge Computing, EC）、机器学习（Machine Learning, ML）及深度学习（Deep Learning, DL）在强化IoV安全框架中的作用。研究分析了这些技术之间的协同效应，凸显其各自能力及在提升威胁检测、响应速度与自适应安全性方面的综合影响。通过真实世界案例研究与实际部署，我们论证了EC、ML及DL当前如何改善IoV系统的安全性与运行效率。本文还识别了IoV安全性进一步发展的关键研究空白与未来方向，包括对可扩展的隐私保护方案及针对新兴网络威胁的稳健防御机制的需求。通过整合EC、ML与DL，本研究为构建能够应对交通生态系统持续变化挑战的自适应、高效且富有弹性的IoV安全基础设施奠定了基础。