Sliding principal component and dynamic reward reinforcement learning based IIoT attack detection

The architecture of the proposed sliding principal component and dynamic reward reinforcement learning (SPC–DRRL) for detecting various IIoT network attacks method is depicted in Fig. 1, whereby there are three main phases, namely, the pre-processing phase, the feature selection phase, and the classification phases.

As shown in the above figure, in the pre-processing phase, we load the TON_IoT Dataset (training set, validation set, and testing sets). The feature values in the…