Maximum correlation based mutual information scheme for intrusion detection in the data networks

作者：

Highlights：

• An optimized MCMIFS based feature selection scheme is proposed.

• The proposed MCMIFS is utilized with Kernel ELM for multiclass intrusion detection.

• The proposed IDS is tested using standard intrusion detection datasets.

• Standard performance metrics i.e. Accuracy, Detection Rate and FPR are utilized.

• Proposed technique outperforms the existing ones for all used cases.

摘要

•An optimized MCMIFS based feature selection scheme is proposed.•The proposed MCMIFS is utilized with Kernel ELM for multiclass intrusion detection.•The proposed IDS is tested using standard intrusion detection datasets.•Standard performance metrics i.e. Accuracy, Detection Rate and FPR are utilized.•Proposed technique outperforms the existing ones for all used cases.

论文关键词：Security,Intrusion detection,Correlation,Mutual information,IDS,Kernel Extreme Learning Machine

论文评审过程：Received 8 January 2020, Revised 12 October 2021, Accepted 12 October 2021, Available online 23 October 2021, Version of Record 28 October 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116089