DEMO: Adaptive Fuzz Testing for Automotive ECUs: A Modular Testbed Approach for Enhanced Vulnerability Detection
Manu Jo Varghese, Frank Jiang, Robin Doss, Adnan Anwar, Abdur Rakib · 2024
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.