Parameter Identification Algorithm for FIR Systems With Quantized Inputs and Binary Outputs Under Data Tampering Attacks

In this article, the parameter identification problem of finite impulse response (FIR) systems with quantized inputs and binary outputs is studied when it is under data tamper attacks. Tampering attacks disrupt the integrity and accuracy of data, and the quantification of inputs and outputs further reduces the information of data. In order to make full use of the information in the data, this article uses the type of persistent regressors to characterize the excitation degree of the input and designs different identification algorithms that remain consistent under attacks. When the excitation of the system by the input is strong, a three-step identification algorithm based on empirical measurement and least squares method is proposed, which can simultaneously conduct consistent identification of the attack strategy and the system parameter vector. When the excitation is weak, a compensation strategy is developed, which introduces a reprocessing module to sacrifice part of the data for consistency estimation of the attack strategy. Subsequently, the estimated attack strategy is used to identify the system parameter vector, and the asymptotic efficiency of the algorithm is given. Finally, simulation results are provided to illustrate the effectiveness of the algorithms designed in this article.