DATA ENHANCEMENT AND DETECTION MODEL FOR INDUSTRIAL INTRUSION DETECTION

Since the collected industrial Internet traffic data has the problems of imbalance in the number of samples of normal traffic and attack traffic, and complex sample features, a Wasserstein generative adversarial network using gradient penalty (WGAN-GP) is proposed and combined with a convolutional neural network (CNN) deep learning intrusion detection method with gated recurrent unit (GRU). We used WGAN-GP data enhancement and used the CNN and GRU hybrid model for deep feature extraction to solve the above problems. Experiments on the model using the CICIDS2017 data set published by the Canadian Institute of Cybersecurity, the results show that compared with different machine learning algorithms, the intrusion detection results using this method are more accurate. The model is validated with the Mississippi State University natural gas pipeline data set, and the results verify the feasibility and effectiveness of the model in an industrial network environment.