Multilevel Fine Fingerprint Authentication Method for Key Operating Equipment Identification in Cyber-Physical Systems

The key equipment in a cyber-physical system (CPS) can be changed or replaced by untrusted equipment, which causes the system to make incorrect decisions, which threatens people's lives and property security. Inspired by the identification of human genes, this research establishes a data fingerprint identification method for equipment. This method provides a security guarantee for the operation of a CPS. Thus, this research makes three contributions. First, it is the first attempt to represent device data information with a mathematical representation model based on multiorder terms. Second, we originally attempt to establish a multigranularity feature extraction method. Then, for the first time, we try to propose different scale matching methods for different granularity features. In addition, an F-404 aircraft example is used to verify the new method. The experimental results show that stealthy false data attacks can bypass the ${\chi <^>{2}}$ detector and destroy the operation of key equipment. However, the method proposed in this article can detect stealthy false data attacks and alert managers so that they can deal with the attacks in time; hence, the method proposed in this article is better than the ${\chi <^>{2}}$ detector. Similarly, we contrast the proposed method with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.