Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1474-1477,1487

Binary tree ensemble intrusion detection method based on stacked sparse autoencoder

Liu Yi1
Yin Ziran1
Hong Zhou2
1. School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
2. Office of Academic Research, Guangzhou City Polytechnic, Guangzhou 510405, China
DOI: 10.19734/j.issn.1001-3695.2018.11.0827

Abstract

In order to solve the problem of classification of large-scale intrusion data, this paper proposed lightGBM binary tree algorithm based on stacked sparse autoencoder. Firstly, it divided the category labels into five categories and constructed into binary tree structures. Then solved the imbalance of data distribution by the upper sampling method, the above processing could separate the large-scale data, so that they could be trained separately. Next, it used the sparse autoencoder network to reduce the feature dimension. Using this method could ensure that time of dimension reduction could save on the basis of extracting deeper features from the original data. Finally, it used the lightGBM ensemble algorithm to classify. And compared to other models, using the lightGBM model could save training time while ensuring classification performance. It used the NSL-KDD dataset to measure the accuracy, precision, recall. And comprehensive evaluation index F1 of the proposed method, which reached an average of 87.42%, 98.20% and 91.31% in five classification, respectively. It is superior to the comparison algorithm and obviously saves the calculation time.

Foundation Support

国家自然科学基金资助项目(61572144)
广州市教育系统创新学术团队资助项目(1201610027)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.11.0827
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Technology of Information Security
Pages: 1474-1477,1487
Serial Number: 1001-3695(2020)05-038-1474-04

Publish History

[2020-05-05] Printed Article

Cite This Article

柳毅, 阴梓然, 洪洲. 基于堆稀疏自编码的二叉树集成入侵检测方法 [J]. 计算机应用研究, 2020, 37 (5): 1474-1477,1487. (Liu Yi, Yin Ziran, Hong Zhou. Binary tree ensemble intrusion detection method based on stacked sparse autoencoder [J]. Application Research of Computers, 2020, 37 (5): 1474-1477,1487. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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