How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protoc...How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data,which not only improves the training efficiency of 2dCNN,but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.展开更多
In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensiona...In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.展开更多
文摘How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data,which not only improves the training efficiency of 2dCNN,but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.
基金Sponsored by the National Natural Science Foundation of China (Grant No.61771223)the Key Research and Development Program of Jiangsu Province(Grant No.SBE2018334)。
文摘In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.
