Enhancing In-Vehicle Network Security Against AI-Generated Cyberattacks Using Machine Learning

Cybersecurity poses a growing threat to technology infrastructure, especially raising concerns for automobile technology. Modern vehicles, reliant on connectivity, face a critical challenge in safeguarding their in-vehicle networks from cyber-attacks. Although the Controller Area Network is a standard for in-vehicle networks, its lack of security features exposes vehicles to vulnerabilities. Especially, the use of AI for offensive purposes further intensifies the threat to automotive technology infrastructure to protect it from cyber-attacks. This study proposes an approach to enhance in-vehicle network security, employing machine learning algorithms to protect against both AI-based generated attacks and traditional attacks. The Conditional Tabular Generative Adversarial Network (CTGAN) is utilized to generate in-vehicle network traffic, which is then combined with benchmark in-vehicle network traffic to create a complex and diverse scenario. The Random Forest model achieved a significant accuracy score for both benchmark in-vehicle network traffic and AI-based traffic, with scores of 0.93 and 0.89, respectively.