Algorithmic Optimization for Accelerated UDS Fuzzing in Cyber-Physical Automotive Networks: The BB-FAST Approach on LIN-Bus

In modern cyber-physical vehicle networks, the security of component-level Electronic Control Units (ECUs) is essential for overall system reliability. While CAN bus security is well-studied, the Local Interconnect Network (LIN) has received less attention despite its growing role in critical functions and diagnostic services (UDS). The inherent constraints of the LIN protocol, specifically its low bandwidth and Master-Slave architecture, make traditional fuzz testing impractical due to extremely long execution times. This paper proposes BB-FAST, an optimized framework for faster vulnerability detection in LIN-based systems. By integrating batch processing and binary search techniques, BB-FAST overcomes communication bottlenecks and enables efficient error localization. Experiments on a physical automotive ECU show that BB-FAST significantly reduces testing time—by 55.56% and 93.44% depending on the diagnostic session—ensuring high efficiency even under frequent reset conditions. By mitigating these physical limitations through algorithmic optimization, this work enables thorough security verification for LIN-based diagnostic interfaces that was previously constrained by protocol latency, thereby enhancing the integrity of cyber-physical automotive networks.