A Novel Approach to Verification of Neural Cyber-Physical Systems

The verification of Cyber-physical System (CPS), particularly those incorporating neural networks for safety-critical functions, remains an ongoing challenge due to the lack of advanced verification and validation frameworks [1]. Current methods are often limited in their scalability and ability to comprehensively verify system-level and component-level properties, leading to potential vulnerabilities in these systems. This issue becomes even more pronounced when dealing with hybrid systems that integrate physical processes and neural network-based controllers [2]. We propose a novel verification framework tailored for complete CPS verification using a decomposition-based approach to address this challenge. Our method performs sequential-distributed verification, ensuring each component adheres to compositional Metric Interval Temporal Logic (MITL). By applying this framework to a model (CPS), we use backward induction to verify that component-level and system-level properties remain within predefined operational ranges, derived from simulation data [4]. Implemented in MATLAB, this approach enhances the verification process by identifying potential failure points across subsystems, providing a scalable solution for verifying complex hybrid systems. This method significantly improves verification accuracy and enables precise identification of faulty components, making it a highly effective tool for robust system design and analysis.