Exploiting Vulnerabilities in Behavioral-Based Driver Authentication Systems: A Comprehensive Security Analysis

Behavioral-based driver authentication systems, while promising, are vulnerable to evasion attacks that can enable vehicle theft. Researchers propose novel attacks, called GAN-CAN, that can fool state-of-the-art models with a perfect attack success rate, allowing an attacker to steal a vehicle in less than 22 minutes.