A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection

作者：

Highlights：

• Using K-Means clustering to reduce the sample size of captured network traffic.

• Development of two drift detection techniques for handling drift.

• Measure severity of concept drift.

摘要

•Using K-Means clustering to reduce the sample size of captured network traffic.•Development of two drift detection techniques for handling drift.•Measure severity of concept drift.

论文关键词：Anomaly detection,SVM,K-Means,Clustering,Concept Drift

论文评审过程：Received 18 June 2020, Revised 4 July 2021, Accepted 2 January 2022, Available online 8 January 2022, Version of Record 10 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116510