Hybrid framework for security evaluation in Internet of Vehicles

Abstract

Advancements in communication technology are driving the rapid evolution of the Internet of Vehicles (IoV) industry, paving the way for future connected vehicle ecosystems. Current vehicle cyber-security efforts primarily concentrate on vulnerabilities within the Controller Area Network (CAN) of existing automobiles. However, the anticipated proliferation of Internet of Vehicles (IoV) capabilities in the near future brings forth a new set of cyber-security challenges. Traditional IoV security analysis methods often focus on either data or dynamic models to assess malicious vehicle behavior, lacking a comprehensive, multidimensional security evaluation approach. In this paper, a novel IoV security analysis framework is proposed, integrating vehicle dynamics models with driving behavior and communication traffic data. The framework employs set-membership filtering algorithms and deep learning techniques to comprehensively assess vehicle status and detect a wide range of security threats, including ARP spoofing, flooding attacks, and speeding, while ensuring adaptability to diverse threat scenarios. Security scores are dynamically generated based on varying threat levels using an enhanced Dempster-Shafer theory, enabling robust threat evaluation. Although the proposed framework is designed for future IoV implementations, its effectiveness is validated through joint simulations conducted in CARLA and OMNeT++, demonstrating its potential to enhance both current and next-generation vehicle networks. Additionally, the proposed framework is designed to be modular, enabling seamless integration with existing connected vehicle security systems and ensuring its relevance for both current and future IoV networks.