Xuecheng Yu1, Yan Huang2, Yu Zhang1, Mingyang Song1, Zhenhong Jia1,3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2023.044999
Abstract 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 T2 and a multilayer convolutional bidirectional long short-term memory (MSC_BiLSTM)… More >
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