Network Intrusion Detection Based on an Improved Long‐Short‐Term Memory Model in Combination with Multiple Spatiotemporal Structures | Zendy

Xiaolong Huang | Zendy

Open Access

Network Intrusion Detection Based on an Improved Long‐Short‐Term Memory Model in Combination with Multiple Spatiotemporal Structures

Author(s) -

Xiaolong Huang

Publication year - 2021

Publication title -

wireless communications and mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.42

H-Index - 64

eISSN - 1530-8677

pISSN - 1530-8669

DOI - 10.1155/2021/6623554

Subject(s) - computer science , intrusion detection system , artificial intelligence , data mining , identification (biology) , constant false alarm rate , artificial neural network , intrusion , pattern recognition (psychology) , term (time) , long short term memory , encoder , recurrent neural network , botany , physics , geochemistry , quantum mechanics , biology , geology , operating system

Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.

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