Network Intrusion Detection Method Based on DBSCAN_GAN

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Network Intrusion Detection Method Based on DBSCAN_GAN_XGBoost

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[1]WANG Zumin,WANG Donghao,LIANG Xia,et al.Network Intrusion Detection Method Based on DBSCAN_GAN_XGBoost[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):44-51.[doi:10.13705/j.issn.1671-6833.2022.03.006]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 03 Page number: 44-51 Column: Public date: 2022-04-10

Title:: Network Intrusion Detection Method Based on DBSCAN_GAN_XGBoost

Author(s):: WANG Zumin¹; WANG Donghao¹; LIANG Xia³; ZOU Qijie¹; QIN Jing²; GAO Bing¹; 1.College of Information Engineering, Dalian University, Dalian 116622, China;
2.College of Software Engineering, Dalian University, Dalian 116622, China;3.Department of Information Engineering, Liaoning Vocational College of Light Industry, Dalian 116100, China

Keywords:: network anomaly detection; density-based spatial clustering of applications with noise; generate adversarial network; extreme gradient boosting; integrated algorithm

CLC:: TN915. 08

DOI:: 10.13705/j.issn.1671-6833.2022.03.006

Abstract:: Due to the unbalanced proportion of abnormal traffic data in network abnormal traffic detection, the model could not fully learn rare attack traffic, which might affect the model training and detection accuracy.To solve this problem, a network intrusion detection model based on DBSCAN_GAN_XGBoost was proposed.When the model expanded rare attack samples, it focused on the noise samples that could more likely cause confusion in machine learning.Firstly, the DBSCAN algorithm was used to cluster the extracted rare attack data categories to generate one or more sub-clusters, and then the samples inside the cluster and the noise samples outside the cluster were extracted.Then, the generative adversarial network model was used to expand the extracted in-cluster samples and noise samples respectively, and to change the original sample proportion.Finally, the reconstructed data set was used to train the XGBoost algorithm based on decision tree classifier, and a complete the detection of abnormal network traffic data.UNSW-NB15 data set was used for comparative experiment, and the experimental results showed that the accuracy, and accuracy of DBSCAN_GAN_XGBoost model were 98.76% and 96.5% respectively, which were 15.63 percentage points and 19.60 percentage points higher than that before sample expansion, and effectively improved the detection accuracy of rare attack categories.

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Last Update: 2022-05-02