Research on elephant flow detection method based on information entropy and improved random forest

Xiaohong Hu; Xianghua Miao; Meiyu Yuan

doi:10.1117/12.2636382

6 May 2022 Research on elephant flow detection method based on information entropy and improved random forest

Xiaohong Hu, Xianghua Miao, Meiyu Yuan

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760V (2022) https://doi.org/10.1117/12.2636382
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

In the increasingly complex network environment, various attacks emerge one after another. Being able to accurately detect elephant flow and mouse flow plays an extremely important role in defending against large-scale network attacks. Aiming at some shortcomings of current methods for detecting elephant flow and mouse flow, this paper proposes a detection method based on information entropy and improved random forest. After preprocessing the data, first calculate the data feature score with information entropy, screen out the truly valuable features according to the score, then put them into the improved random forest classifier, and finally get the detection results. In this paper, the grid search algorithm is used to optimize the tree and depth of random forest tree, so that the detection results can be obtained quickly and accurately. Experiments show that due to the significant difference between the data characteristics of elephant flow and mouse flow, this method can effectively identify elephant flow and mouse flow. The accuracy rate is 97.93%, the precision rate is 99.99%, the recall rate is 97.91%, and the F1-score is 98.94%, which is improved compared with other algorithms.

Citation Download Citation

Xiaohong Hu, Xianghua Miao, and Meiyu Yuan "Research on elephant flow detection method based on information entropy and improved random forest", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760V (6 May 2022); https://doi.org/10.1117/12.2636382

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