Aiming at the inaccuracy of traditional abnormal flow detection model in the industrial control system, an abnormal flow detection model based on Convolutional Neural Network (CNN) was proposed. The proposed model was based on CNN algorithm and consisted of a convolutional layer, a full connection layer, a dropout layer and an output layer. Firstly, the actual collected network flow characteristic values were scaled to a range corresponding to the grayscale pixel values, and the network flow grayscale map was generated. Secondly, the generated network traffic grayscale image was put into the designed convolutional neural network structure for training and model tuning. Finally, the trained model was used to the abnormal flow detection of the industrial control network. The experimental results show that the proposed model has a recognition accuracy of 97.88%, which is 5 percentage points higher than that of Back Propagation (BP) neural network with the existing highest accuracy.

Most face landmark detection algorithms include two steps:face detection and face landmark localization, increasing the processing time. Aiming at the problem, a one-step and real-time algorithm for multi-face landmark localization was proposed. The corresponding heatmaps were generated as data labels by the face landmark coordinates. Deep residual network was used to realize the early feature extraction of image and feature pyramid network was used to fuse the information features representing receptive fields with different scales in different network depths. And then based on intermediate supervision, multiple landmark prediction networks were cascaded to realize the one-step coarse-to-fine facial landmark regression without face detection. With high accuracy localization, a forward propagation of the proposed algorithm only takes about 0.0075 s (133 frames per second), satisfying the requirement of real-time facial landmark localization. And the proposed algorithm has achieved the mean error of 6.06% and failure rate of 11.70% on Wider Facial Landmarks in-the-Wild (WFLW) dataset.