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Network Intrusion Traffic Detection Based on Feature Extraction 被引量:1
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作者 Xuecheng Yu Yan Huang +2 位作者 Yu Zhang Mingyang Song Zhenhong Jia 《Computers, Materials & Continua》 SCIE EI 2024年第1期473-492,共20页
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
关键词 Network intrusion traffic detection PCA hotellings T^(2) BiLsTM
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On high-dimensional change point problem
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作者 JIN BaiSuo PAN GuangMing +1 位作者 YANG Qing ZHOU Wang 《Science China Mathematics》 SCIE CSCD 2016年第12期2355-2378,共24页
New statistics are proposed to estimate and test the structural change when the data dimension is comparable to or larger than the sample size. Consistency of the new statistic in estimating the change point position ... New statistics are proposed to estimate and test the structural change when the data dimension is comparable to or larger than the sample size. Consistency of the new statistic in estimating the change point position is established under the alternative hypothesis. The asymptotic distribution of the new statistic in testing the existence of a change point is obtained under the null hypothesis. Some simulation results are presented which show that the numerical performance of our method is satisfactory. The method is illustrated via the analysis of the house price index of US. 展开更多
关键词 change point high-dimensional statistics inference hotelling's T^2 statistic
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多变量半连续数据的似然比检验 被引量:2
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作者 鲁亚会 刘爱义 江涛 《系统科学与数学》 CSCD 北大核心 2021年第11期3254-3266,共13页
随着信息技术的发展,多变量半连续数据出现在越来越多的研究领域中,其主要特征是多变量数据中的每一个变量都含有过多的零值.然而相较于单变量半连续数据,目前却还未有学者关注于多变量半连续数据的假设检验问题.因此,文章主要研究多变... 随着信息技术的发展,多变量半连续数据出现在越来越多的研究领域中,其主要特征是多变量数据中的每一个变量都含有过多的零值.然而相较于单变量半连续数据,目前却还未有学者关注于多变量半连续数据的假设检验问题.因此,文章主要研究多变量半连续数据的两总体假设检验问题.针对多变量半连续数据,文章构建一种多元Bernoulli-Normal模型,并提出一种基于多元复合原假设的似然比检验方法.由数据模拟结果表明,相较于经典的Hotelling's T^(2)检验方法,似然比方法具有较低的第Ⅰ类错误率和较高的检验功效.此外,将此方法应用到饮食摄入量CHEF实例数据中,结果表明所提出的方法能够对干预措施的有效性进行评估. 展开更多
关键词 多元半连续数据 多元Bernoulli-Normal模型 多元复合原假设 似然比检验 hotellings T^(2)检验
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