关键词: 工控网络, 生成式对抗网络, 卷积神经网络, 极限学习机, 入侵检测

Abstract: Aiming at the problems of uneven distribution of data types and high dimensions in the current industrial control network environment, this paper uses the data augmentation method of auxiliary classifier generative adversarial network (ACGAN) to enhance the data set, and adopts a convolutional neural network (CNN) and extreme learning machine (ELM) hybrid model for feature extraction and classification of the data set. Through the simulation experiments on the NSL-KDD data set, the accuracy rate of the hybrid model reaches 99.26%, and the false negative rate is lower than 0.625%, which are better than traditional machine learning algorithms. At the same time, the natural gas pipeline data set of Mississippi State University is used for experimental simulation verification, with an accuracy rate of 99.18% and a false negative rate lower than 0.621%. This model is also applicable in complex industrial control environment, and broadens the research idea of industrial intrusion detection.