Stacking Ensemble Learning Network Attack Detection Based on Industrial Processes in CPS-Enabled Smart Water Conservancy

Abstract

As communication networks and the Internet of Things (IoT) converge in water critical infrastructure, it facilitates smart online monitoring of water supply systems, but it also significantly increases the incidence of cyber attacks. Additionally, it becomes especially challenging to detect attacks timely as they become more sophisticated and multifaceted. To address this issue, we propose a stacking ensemble network detection model based on industrial processes in water treatment systems, where the attacks are identified through recognizing anomalies caused by the underlying dynamics of the industrial control system. In the proposed detection model, we utilize a multilayer perceptron (MLP) to facilitate multi-module training, taking into consideration the multi-stage process of the water conservancy system. Furthermore, we adopt Long Short-Term Memory Recurrent Neural Network (LSTM) to effectively integrate the predicted results. To optimize the performance of this model, we leverage a Population Based Training (PBT) of Neural Networks to finetune the parameters. For each sensor parameter, the residuals between the predicted and measured data are classified using a sliding window-based dynamic threshold to identify anomalies. Based on the Real Safe Water Treatment (SWaT) system dataset, we evaluate the accuracy of our proposed scheme with three other schemes. The experimental comparison results show that the accuracy of our scheme is better than those of all the other three schemes.