Detection of redundant traffic in large-scale communication networks based on logistic regression
by Xin Wen; Liyu Huang; Yin Zheng; Hailin Zhao
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 1, 2024

Abstract: In order to improve the traffic precision of network redundant traffic detection methods and reduce the time consumption of traffic classification, this paper proposes a large-scale redundant traffic detection method based on logical regression. Firstly, the logical regression architecture is analysed, and a feature extractor is constructed to extract redundant traffic features. Secondly, the weight matrix of the linear transformation between layers to be trained is obtained. Then, Gini coefficient is selected to determine the dispersion degree of redundant traffic, and redundant traffic classification function is constructed. Redundant traffic detection results are obtained through logical regression algorithm to complete network redundant traffic detection. The results show that the traffic classification time of this method is 53 ms; the precision rate is as high as 99%, which shows that the network redundant traffic detection method in this paper is effective.

Online publication date: Tue, 19-Mar-2024

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