Fast network intrusion detection system using adaptive binning feature selection

被引：0

作者：

Liu J. ^{[1
]}

Gao Y. ^{[1
]}

机构：

[1] State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2021年 / 48卷 / 01期

关键词：

Ensemble learning; Feature selection; Information gain; Intrusion detection; LightGBM algorithm;

D O I：

10.19665/j.issn1001-2400.2021.01.020

中图分类号：

学科分类号：

摘要：

Aiming at the problems of the low detection rate of traditional intrusion detection systems and the long training and detection time of intrusion detection systems based on deep learning,an adaptive binning feature selection algorithm using the information gain is proposed,which is combined with LightGBM to design a fast network intrusion detection system.First,the original data set is preprocessed to standardize the data; then the redundant features and noise in the original data are removed through the adaptive binning feature selection algorithm,and the original high-dimensional data are reduced to the low-dimensional data,thereby improving the accuracy of the system and reducing the training and detection time; finally,LightGBM is used for model training on the training set selected by the characteristics to train an intrusion detection system that can detect attack traffic.Through verification on the NSL-KDD data set,the proposed feature selection algorithm only takes 27.35 seconds in feature selection,which is 96.68% lower than that by the traditional algorithm.The designed intrusion detection system has an accuracy rate of 93.32% on the test set,and its training time is low.Compared with the existing network intrusion detection system,the accuracy rate of the proposed system is higher,and its model training speed is faster. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：176 / 182

页数：6

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