An Attack Prediction and Recognition Method for Water Treatment System Based on One-dimensional Convolutional Neural Network and Cumulative Sum
- Resource Type
- Conference
- Authors
- Hu, Xiangdong; Liu, Lang
- Source
- 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) Neural Networks, Information and Communication (NNICE), 2024 4th International Conference on. :715-718 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Training
Time series analysis
Predictive models
Convolutional neural networks
Long short term memory
water treatment system
1D-CNN
CUSUM
prediction
recognition
- Language
An attack prediction and recognition method for water treatment system based on one-dimensional convolutional neural network (1D-CNN) and cumulative sum (CUSUM) is proposed in this paper. Firstly, a 1D-CNN is employed as a time series predictor to establish an independent prediction model for each attack point, followed by training and learning. Subsequently, the deviation between predicted values and actual values is compared with a threshold value using CUSUM statistics to identify anomalies within the water treatment system. Experimental evaluations conducted on the Safe Water Treatment (SWaT) Test Bench demonstrate that 1D-CNN exhibits superior predictive performance compared to other models. Furthermore, CUSUM successfully detects and identifies all listed attacks based on statistical bias.