Currently, most anomaly detection approaches in industrial control systems (ICSs) use network event logs to build models, and current unsupervised machine learning methods rarely use spatiotemporal correlations and other dependencies between multiple variables (sensors/actuators) in a system to detect anomalies. Most of the existing anomaly detection technologies simply compare the current states with the predicted normal range. Due to the highly dynamic characteristic of industrial control systems, it is insufficient to simply compare the current states with the predicted normal range. As a result, these approaches have low detection rates for unknown or new types of attacks. In view of these shortcomings, this paper presents a network model for predicting sensor/controller parameters in industrial control systems. To predict the parameter values of the sensors and controllers more accurately, the 1D convolutional neural network (1D_CNN) and gated recurrent unit (GRU) are combined to fully learn the spatiotemporal correlation and other dependencies between the parameter values of the sensors and controllers at each moment. An abnormal state detection method based on the calculation of the statistical deviation is proposed to realize the anomaly detection of industrial control systems. The model is validated on the Secure Water Treatment (SWaT) dataset. The precision, recall and F1 scores are used to evaluate the effectiveness of this method in anomaly detection on the SWaT dataset. The experimental results show that the average precision and recall of this method are 0.99 and 0.85, respectively, and that the average F1 score is 0.91. The experimental results show that the proposed method can be successfully applied to anomaly detection systems in industrial control systems with lower false positive rates. INDEX TERMS Auto-encoder, 1D convolutional neural network, gated recurrent unit, industrial control system, anomaly detection, SWaT dataset.

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Most of the current image edge detection methods rely on manually features to extract the edge, there are often false and missed detections when the image has adverse interference. The surface of mechanical parts is smooth, when taking photos in the industrial field, it is easy to have specular reflection and shadow at the same time, which will affect the edge detection results. In order to achieve excellent edge detection performance, we propose a semantic segmentation model based on encoder-decoder structure. It adopts joint learning strategy, using two decoders to process image decomposition task and segmentation task respectively, and sharing their parameters to eliminate the influence of illumination, so as to improve segmentation performance. In the training phase, the asymmetric convolution and BN fusion are combined to improve the detection efficiency. In addition, we built a gear part dataset for experimentation. The result shows that in the task of edge detection of mechanical parts affected by illumination, our method has better performance than classical method.

Summary Intrusion detection is essential to prevent damage to computer systems. However, in recent years, with the development of the network, many complex attack types have appeared, and it has become increasingly difficult to obtain high detection rates and low false alarm rates. In addition, traditional heavily hand‐crafted evaluation datasets for network intrusion detection have not been practical. This article proposes an intrusion detection method based on hierarchical feature learning, which can automatically learn traffic features. The method first learns the byte‐level features of network traffic through one‐dimensional convolutional neural networks and then learns session‐level features using stacked denoising autoencoder. The experiment analyzed the model structure and compared it with other methods. Experiments prove that the method in this article has high accuracy and low false alarm rate.

Given the shortcomings of conventional machine vision-based surface defect detection methods, including their low accuracy, long development cycle, and poor generalization ability, this paper proposes a surface defect detection model based on the convolutional block attention module and atrous convolution. This model combines the surface defect segmentation task of the product with the classification task, obtains contextual information of the image at multiple scales using atrous spatial pyramid pooling, and then uses the convolutional block attention module to reallocate the weighting of the network to enhance focus on the defect area and improve the discrimination of extracted features. In addition, atrous convolution was introduced in the deep network to simplify the model when used in defect segmentation tasks and enhances the real-time performance of the model defect detection method. Experiments show the superior accuracy and real-time performance of the proposed model when compared with current mainstream surface defect detection methods and indicate its wide applicability in the detection of surface defects in industrial products.