An Ensemble Learning Method with Feature Fusion for Industrial Control System Anomaly Detection

With the deepening of the integration of industrialization and informatization, information technology brings great risks to the safe operation of industrial control system. How to effectively detect the network anomaly behavior in the industrial control system is the key problem of industrial control security research. This paper proposes an ensemble learning method with feature fusion to solve the problem of anomaly classification of network data. Computational results illustrate that the method proposed in this paper has excellent detection effect on different kinds of network attacks in the experiment and improves the detection accuracy.