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

2026-04-11

AI Summary

This study addresses the critical security challenges confronting Internet of Vehicles (IoV) systems—stemming from their high degree of connectivity—including data privacy breaches, cyberattacks, and systemic vulnerabilities. To tackle these issues, this work proposes a novel collaborative security framework that synergistically integrates edge computing with artificial intelligence, specifically leveraging machine learning and deep learning techniques. For the first time, this approach systematically unifies these domains to enable efficient, adaptive threat detection and response. The proposed framework not only significantly enhances the security posture and operational efficiency of IoV systems but also establishes a scalable, privacy-preserving paradigm for resilient security, thereby laying a foundational architecture for future IoV deployments.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: ML and AI systems for Web, mobile and WoT
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Internet of Vehicles
• security challenges
• cyber threats
• data privacy
• system vulnerabilities

Innovation

• Edge Computing
• Machine Learning
• Deep Learning
• IoV Security
• Adaptive Security