Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial control systems based on improved comparative learning SimCLR. Firstly, a feature extraction network is trained on SimCLR using unlabeled data; a linear classification layer is added to the trained feature extraction network model; and a small amount of labeled data is used for supervised training and fine-tuning of the model parameters. The trained model is simulated on the Secure Water Treatment (SWaT) dataset and the publicly available industrial control dataset from Mississippi State University, and the results show that the method has better results in all evaluation metrics compared with the deep learning algorithm using supervised learning directly, and the comparative learning has research value in industrial control system intrusion detection.

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

In recent years, deep learning has been widely used in the field of image visualization, and has attracted the attention of researchers. The main component of iron tailings is SiO2, In the process of quenching and tempering slag from iron tailings, melted SiO2 particles will randomly walk in the crucible, so that how to intelligently identify and track targets (SiO2 particles) in sequence image to promote the low consumption and efficient production of high value-added slag cotton has become an urgent problem. First of all, aims at the temperature requirements of slag cotton preparation process, using intelligent algorithm with deep learning and hierarchical clustering, in-depth analysis of the visual information of the high temperature melting process of SiO2 particles. Through centroid tracking and positioning technology, intelligent edge feature extraction technology and fitting method of experimental data through high temperature melting process of SiO2 particles quantitative characterization functions of visual characteristics of SiO2 particles melting process under high temperature environment were obtained. Approximation of the melting process of iron tailings in high temperature environments and embed this function into the static mathematical model of process thermal compensation an accurate thermal dynamic intelligent compensation model for slag cotton preparation process has realized.