Anomaly detection in industrial control systems using evolutionary-based optimization of neural networks
Amin Mansouri, Babak Majidi, Abdolah Shamisa · Communications on Advanced Computational Science with Applications · 2017
Industrial control systems are increasingly used for control and monitoring of important infrastructure. Machine learning algorithms have the ability to discover patterns in large amounts of data and to create diagnosis models based on these patterns. Since modelling a large amount of unlabeled data is costly and time-consuming, the automated machine learning methods have the ability to detect anomalies in industrial control systems effectively. In this paper, first, twenty-four machine learning algorithms are evaluated for anomaly detection in gas distribution control network. Then dimensionality reduction algorithms are used to improve the accuracy of anomaly detection. Finally, by using an evolutionary based optimization for training a neural network, a new algorithm for prediction of anomalies in the SCADA system with high accuracy is proposed. The experimental results show that the proposed algorithm has the ability to detect the anomalies in the gas distribution control network with 97.5% accuracy.