With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(...With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.展开更多
Resistivity will have different response characteristics to the hydraulic fracture propagation process. In this work, a resistivity testing system for hydraulic fracturing specimens was established. Resistivity and ac...Resistivity will have different response characteristics to the hydraulic fracture propagation process. In this work, a resistivity testing system for hydraulic fracturing specimens was established. Resistivity and acoustic emission(AE) information were jointly analysed to determine the dynamic response characteristics of resistivity during hydraulic fracture propagation. The results show that the water and fracture exert a competitive influence on the connection structure of the circuit, and there are two significant peak resistivity points in the curve, presenting a double peak therein. The peak resistivity data of the specimen with a larger fracture area are much different from the initial value. With the increase of the rate of injection, the range of variation of the highest value that can be reached with the specimen resistivity decreases. High resistivity rates or high resistivity fluctuations exhibit rapid a release of fracture energy. The fracture failure mode dominated by shear fractures makes the formation produce a “series+parallel” electrical connection structure;a calculation model of formation resistivity based on shear and tensile failure was proposed to characterize the proportion of different types of hydraulic fractures and elucidate the control effect of matrix resistivity on the electrical performance of the overall circuit structure.展开更多
基金supported by Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD).
文摘With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.
基金supported by the National Key R&D Program of China (No. 2018YFC0807805)the National Natural Science Foundation of China (No. 52074049)。
文摘Resistivity will have different response characteristics to the hydraulic fracture propagation process. In this work, a resistivity testing system for hydraulic fracturing specimens was established. Resistivity and acoustic emission(AE) information were jointly analysed to determine the dynamic response characteristics of resistivity during hydraulic fracture propagation. The results show that the water and fracture exert a competitive influence on the connection structure of the circuit, and there are two significant peak resistivity points in the curve, presenting a double peak therein. The peak resistivity data of the specimen with a larger fracture area are much different from the initial value. With the increase of the rate of injection, the range of variation of the highest value that can be reached with the specimen resistivity decreases. High resistivity rates or high resistivity fluctuations exhibit rapid a release of fracture energy. The fracture failure mode dominated by shear fractures makes the formation produce a “series+parallel” electrical connection structure;a calculation model of formation resistivity based on shear and tensile failure was proposed to characterize the proportion of different types of hydraulic fractures and elucidate the control effect of matrix resistivity on the electrical performance of the overall circuit structure.
