Optimal attack strategy for binary measurements-based Hammerstein system identification subject to data tampering attacks

This paper studies the formulation for an optimal attack strategy, which aims to maximize the identification error of Hammerstein systems with binary measurements subject to data tampering attacks. First, the convergence of the parameter estimates under attack is analyzed, and the absolute error between the estimated value and the true value is used as the objective function. Second, an optimization model with constraints on the maximum data tampering rate and the average data tampering rate is established to maximize the objective function. Then, the sine-cosine optimization algorithm is used to search for the optimal solution that meets the constraints, and its performance is compared with other algorithms. Finally, the effectiveness of the proposed method is verified by a numerical simulation.