<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To protect network security, this paper develops a large-scale network anomalous traffic identification algorithm that utilizes the linear discriminant analysis method to intercept network anomalous traffic. Firstly, the classification of large-scale network anomalous traffic is explored, and the SSAE algorithm is combined with the feature selection of large-scale network traffic on the basis of network flow feature extraction. Secondly, data dimensionality reduction of network anomalous traffic using linear discriminant analysis and feature selection of large-scale network traffic based on SSAE to identify network anomalous traffic. Finally, the CICIDS2017 dataset and the NSL-KDD dataset are used to experimentally analyze the effect and performance of feature selection and anomaly identification algorithms. The results show that the classification accuracy of the feature selection algorithm is 0.989, the 10-dimensional optimal features selected are (12,6,5,38,29,3,33,35,36,40), and the recognition result is 0.803 for normal network traffic and 0.197 for anomalous traffic, with an overall recognition error of 0.003, and a performance of more than 0.988.</p>
</abstract>

To protect network security, this paper develops a large-scale network anomalous traffic identification algorithm that utilizes the linear discriminant analysis method to intercept network anomalous traffic. Firstly, the classification of large-scale network anomalous traffic is explored, and the SSAE algorithm is combined with the feature selection of large-scale network traffic on the basis of network flow feature extraction. Secondly, data dimensionality reduction of network anomalous traffic using linear discriminant analysis and feature selection of large-scale network traffic based on SSAE to identify network anomalous traffic. Finally, the CICIDS2017 dataset and the NSL-KDD dataset are used to experimentally analyze the effect and performance of feature selection and anomaly identification algorithms. The results show that the classification accuracy of the feature selection algorithm is 0.989, the 10-dimensional optimal features selected are (12,6,5,38,29,3,33,35,36,40), and the recognition result is 0.803 for normal network traffic and 0.197 for anomalous traffic, with an overall recognition error of 0.003, and a performance of more than 0.988.

A linear discriminant analysis-based algorithm for identifying anomalous traffic in large-scale networks

Anhui University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To protect network security, this paper develops a large-scale network anomalous traffic identification algorithm that utilizes the linear discriminant...

A linear discriminant analysis-based algorithm for identifying anomalous traffic in large-scale networks

Data publikacji: 18 lis 2023

Zakres stron: -

Otrzymano: 17 lut 2023

Przyjęty: 29 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01173

Słowa kluczowe
Linear discriminant analysis, SSAE algorithm, Feature selection, Data dimensionality reduction, Anomalous flow identification

© 2023 Qiubing Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A linear discriminant analysis-based algorithm for identifying anomalous traffic in large-scale networks

Data publikacji: 18 lis 2023

Zakres stron: -

Otrzymano: 17 lut 2023

Przyjęty: 29 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01173

Słowa kluczoweLinear discriminant analysis, SSAE algorithm, Feature selection, Data dimensionality reduction, Anomalous flow identification

© 2023 Qiubing Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Linear discriminant analysis, SSAE algorithm, Feature selection, Data dimensionality reduction, Anomalous flow identification