Core Concepts
Integrating digital twin technology and AI models enhances vehicular cybersecurity in VANETs.
Abstract
The rapid evolution of Vehicular Ad-hoc Networks (VANETs) has brought significant advancements to intelligent transportation systems (ITS), improving road safety and communication. However, vulnerabilities in Roadside Units (RSUs) pose risks like cyberattacks, leading to traffic congestion and vehicle malfunctions. Existing methods struggle with dynamic attacks, prompting the need for a novel framework combining digital twin technology with AI for enhanced security. This framework enables real-time monitoring, efficient threat detection, reduced data transmission delay, and improved energy efficiency. By optimizing RSU efficiency, it outperforms existing solutions in resource management and attack detection effectiveness.
Stats
Our solution reduces RSU load and data transmission delay.
The proposed framework achieves an optimal balance between resource consumption and high attack detection effectiveness.
Quotes
"Our study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs."
"Our solution significantly advances green communications in VANETs by reducing computational demands."