Research on Intrusion Detection Based on Stacked Autoencoder and Long-short Memory

Authors

Lin Shuo, 1. Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; ;
An Lei, 1. Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; ;
Zhijun Gao, 1. Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; ;
Shan Dan, 1. Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; ;
Wenli Shang, 2. Department of Industrial Control Network and System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110168, China; ;3. Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China; ;4. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China;

Abstract

Abstract: As network attacks increasingly hidden, intelligent and complex. Simple machine learning cannot deal with attacks timely. A deep learning method based on the combination of SDAE and LSTM is proposed. Firstly, the distribution rules of network data are extracted intelligently layer by layer by SDAE, and the diverse anomaly features of high-dimensional data ate extracted by using coefficient penalty and reconstruction error of each coding layer. Then, LSTM’ s memory function and the powerful learning ability of sequence data are used to classify learning depth. Finally, the experiments are carried out with the UNSW-NB15 data set, which is analyzed by adjusting the time step. The experimental results show that the model has higher detection accuracy and lower false alarm rate.

Recommended Citation

Shuo, Lin; Lei, An; Gao, Zhijun; Dan, Shan; and Shang, Wenli (2021) "Research on Intrusion Detection Based on Stacked Autoencoder and Long-short Memory," Journal of System Simulation: Vol. 33: Iss. 6, Article 7.
DOI: 10.16182/j.issn1004731x.joss.20-0112
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss6/7

First Page

1288

Revised Date

2020-05-19

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0112

Last Page

1296

CLC

TP393

Recommended Citation

Lin Shuo, An Lei, Gao Zhijun, Shan Dan, Shang Wenli. Research on Intrusion Detection Based on Stacked Autoencoder and Long-short Memory[J]. Journal of System Simulation, 2021, 33(6): 1288-1296.

Corresponding Author

Zhijun Gao,

DOI

10.16182/j.issn1004731x.joss.20-0112