Impact of Intelligent Technologies on IoV Security: Integrating Edge Computing and AI

TL;DR

This paper reviews how Edge Computing, Machine Learning, and Deep Learning enhance security in the Internet of Vehicles, addressing threats, vulnerabilities, and future research needs.

Contribution

It provides a comprehensive survey of integrating EC, ML, and DL for IoV security, highlighting current applications and future research directions.

Findings

EC, ML, and DL improve threat detection and response in IoV.

Real-world case studies demonstrate enhanced security and efficiency.

Identifies key research gaps and future directions in IoV security.

Abstract

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.