Research on Intrusion Identification of Industrial Control Systems Based on Improved Denoising Autoencoder Model
Traditional intrusion recognition methods for industrial control systems often suffer from issues such as missed and false alarms when facing complex attack patterns and covert attack methods.Therefore,an improved denoising autoencoder model based intrusion recognition method for industrial control systems is proposed.Firstly,process and extract data features from industrial control systems,perform binning operations,and construct an improved denoising autoencoder intrusion recognition model.Then,a Softmax function classifier is added after the hidden layer node for classification,completing the operation of intelligent recognition of industrial control system intrusion.The results show that the designed method can accurately identify the number of various types of intrusion behaviors in the test dataset,with high recognition accuracy,and can achieve effective protection for industrial control systems.
intrusion recognition methodindustrial control systemmodel trainingimproved denoising autoencoder model
万方数据
维普
