An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning

作者：

Highlights：

• Proposing a feature selection algorithm based on local search and pigeon optimizer.

• Introducing an ensemble technique to used in IDS for IoT cyber-Attack.

• Investigating Tabu and Hill Climbing search for improving PIO optimizer performance.

• Anomaly detection based on one class classifiers has been explored.

摘要

•Proposing a feature selection algorithm based on local search and pigeon optimizer.•Introducing an ensemble technique to used in IDS for IoT cyber-Attack.•Investigating Tabu and Hill Climbing search for improving PIO optimizer performance.•Anomaly detection based on one class classifiers has been explored.

论文关键词：00-01,99-00,Ensemble learning,LS-PIO,NIDS,One-class classifiers,PIO

论文评审过程：Received 24 January 2022, Revised 27 August 2022, Accepted 30 August 2022, Available online 5 September 2022, Version of Record 27 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118745