基于多变量时序数据的对抗攻击与防御方法

To ensure the security of the attack detection model of time series data, an adversarial attack and adversarial defense method based on multivariate time series data was proposed. First, the escape attack implemented in the test phase was designed for the autoencoder-based attack detection model. Second, according to the designed adversarial attack samples, the adversarial defense strategy based on the Jacobian regularization method was proposed. The Jacobian matrix in the calculation model training process was taken as the regular term in the objective function to improve the defense capability of the deep learning model. The attack effects of the proposed attack methods and the defense effect of the proposed adversarial defense method were verified on the BATADAL dataset of industrial water treatment.