A network intrusion detection method based on SSA-BRF

doi:10.3969/j.issn.1000-1565.2022.05.014

Abstract

Abstract: In order to solve the problem that the selection of hyper-parameters of random forest(RF)will have a great impact on the classification results of RF, an optimization method of RF hyper-parameters based on sparrow search algorithm(SSA)is proposed to help RF find a set of excellent hyper-parameters. Balanced random forest(BRF)is used to solve the problem that RF is difficult to accurately identify minority data, so as to improve the recall rate of the model for minority data. Finally, SSA-BRF model is constructed by integrating SSA and BRF, and the classification effect of the model is verified in CIC-IDS-2017 data set. Experimental results show that compared with RF, the accuracy, macro recall and macro F1 score of SSA-BRF are improved by 9.57%, 26.62% and 0.17 respectively. This model can improve the performance of network intrusion detection system to a certain extent.

Key words: sparrow search algorithm, hyperparameter optimization, balanced random forest, imbalanced data classification, CIC-IDS-2017

CLC Number:

TP393.08

WEI Mingjun, ZHANG Xinnan, LIU Yazhi, ZHOU Taiyu. A network intrusion detection method based on SSA-BRF[J]. Journal of Hebei University(Natural Science Edition), 2022, 42(5): 552-560.

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