中圖分類號(hào)： TP391.9
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.09.005
引用格式： 宗學(xué)軍，宋治文，何戡，等. 基于1d-MSCNN+GRU的工業(yè)入侵檢測(cè)方法研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(9)：25-31.

Research on industrial intrusion detection method based on 1d-MSCNN+GRU model

Abstract： In order to solve the problem that traditional machine learning methods rely heavily on features, and traditional convolutional neural network only extracts important local features to complete recognition and classification, and the convergence speed is slow, an intrusion detection method combining 1-dimensional multiscale convolutional neural network and gated recurrent unit is proposed. In this method, 1-dimensional multiscale convolutional neural network is used to enhance the ability to capture features, speed up the convergence speed, and the gating cycle unit is used to grasp the spatial features, reduce the expansion of the number of channels and reduce the data dimension. The KDD CUP 99 data set and the natural gas pipeline data set of the University of Mississippi are used for simulation experiments. The results show that the method has higher intrusion detection performance and better generalization ability than the classical machine learning classifier.

Key words : 1-dimensional Multiscale Convolutional Neural Networks(1d-MSCNN)；Gated Recurrent Unit(GRU)；intrusion detection；deep learning