Network Intrusion Detection Model Based on Space-time Fusion Features and Attention Mechanism

被引：0

作者：

Wu, Yali ^{[1
]}

Huang, Liting ^{[1
,2
]}

Qi, Jinjin ^{[2
]}

Quan, Xiaoxiao ^{[2
]}

机构：

[1] Xian Univ Technol, Sch Automat & Informat Engn, Xian 710048, Peoples R China

[2] Shaanxi Prov Key Lab Complex Syst Control & Intel, Xian 710048, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

关键词：

Network Intrusion Detection; Convolution Neural Network; Long Short-term Memory Network; Attention Mechanism; CLT-net;

D O I：

10.1109/CCDC52312.2021.9602544

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To solve the problem of low performance of network intrusion detection, a deep learning intrusion detection model based on space-time fusion features and attention mechanism-CLT-net is proposed. In this model, space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network, and attention module is added to calculate the importance of the input features, and softmax function is used for classification. Through a large number of simulation experiments on NSL-KDD data sets, CLT-net has significantly improved the convergence ofthe training set and the accuracy ofthe test set. Compared with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9% respectively. Research shows that this model has great potential in the application field of network intrusion detection.

引用

页码：2533 / 2538

页数：6

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