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This demonstration introduces an adaptive fuzzing physical test bed aimed at identifying vulnerabilities within automotive systems, specifically focusing on the Controller Area Network (CAN) bus. By employing "Automated Reverse Engineering-Guided Fuzzing" (ARE-GF), our framework evaluates the security resilience of the CAN network against sophisticated attacks. The demo showcases live demonstrations of the fuzzing process, the creation of the test bed using cost-effective electrical components, real-time ECU response analysis, and examples of discovered vulnerabilities, providing insights into advanced automotive cybersecurity testing methodologies.
Varghese et al. (Sun,) studied this question.