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 Controller Area Network(CAN) 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 Batch-based Binary-search Fuzzing and Accelerated Security Testing (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. Empirical evaluations on a physical automotive ECU demonstrate that BB-FAST achieves a significant reduction in testing time—up to 97.7% compared to traditional sequential methods. Notably, in scenarios involving critical controller failures, BB-FAST outperformed optimized batch-based approaches by 64.2% through its logarithmic error localization logic. 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.