Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy

Yong Sun, Huakun Que, Qianqian Cai, Jingming Zhao, Jingru Li, Zhengmin Kong and Shuai Wang
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Yong Sun: Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Huakun Que: Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Qianqian Cai: Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Jingming Zhao: Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Jingru Li: Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Zhengmin Kong: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Shuai Wang: China Southern Power Grid Power Technology Co., Ltd., Guangzhou 510600, China

Energies, 2022, vol. 15, issue 13, 1-13

Abstract: This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained.

Keywords: network intrusion detection; machine learning; borderline SMOTE; information gain ratio (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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