A Method for Detecting Data Tampering Attacks Based on Prior Information

Addressing the challenge of effectively detecting data tampering attacks in cyber-physical systems, this paper proposes an attack detection method based on prior information for the identification of a class of Hammerstein nonlinear systems measured by binary sensors. This method leverages the periodic structure of the system inputs and the statistical properties of the binary observation data to characterize the asymptotic properties of the parameter estimators; furthermore, by incorporating prior information regarding the system parameters, it constructs a detection criterion that enables the effective identification of attack behaviors. To enhance the computational efficiency of the algorithm in practical applications, a Multilayer Perceptron (MLP) is employed to approximate the implicit nonlinear inverse mapping, thereby circumventing the numerical difficulties associated with directly solving systems of nonlinear equations. On a theoretical level, the asymptotic distributions of the detection algorithm’s false alarm rate and missed detection rate are derived, and a systematic analysis is conducted on how detection performance is affected by factors such as system input period, prior information scope, and data length. Numerical simulations validate the efficacy of the proposed method; the results demonstrate that as the data length increases, both the false alarm rate and the missed detection rate of the algorithm decrease. Moreover, a broader scope of prior information leads to a lower false alarm rate but a higher missed detection rate, thereby illustrating the “double-edged sword” effect of prior information in the context of attack detection. This study provides a theoretical foundation and technical support for attack detection in nonlinear systems operating under conditions of data constraints and security threats.