A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection

Chen Chen; Yajiang Qi; Xiaoyan Ye; Guanghua Wang; Lintao Yang; Haiyue Ji

doi:10.1117/12.2653422

8 December 2022 A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection

Chen Chen, Yajiang Qi, Xiaoyan Ye, Guanghua Wang, Lintao Yang, Haiyue Ji

Author Affiliations +

Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 1247411 (2022) https://doi.org/10.1117/12.2653422
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China

Abstract

In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy. A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection is proposed. Firstly, the convolution neural network is used to extract the deep information in the original data set, which is normalized as the input of the model. In constructing base classifiers, different heterogeneous model combinations are used to enhance the diversity of base classifiers. Experiments on NSL-KDD dataset show that the proposed model can comprehensively improve the detection accuracy, accuracy, recall and F1-score.

Citation Download Citation

Chen Chen, Yajiang Qi, Xiaoyan Ye, Guanghua Wang, Lintao Yang, and Haiyue Ji "A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 1247411 (8 December 2022); https://doi.org/10.1117/12.2653422

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