Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system

作者：

Highlights：

• The hybrid ML method consists of two techniques: Differential Evolution (DE) and Extreme Learning Machine (ELM). The DE is responsible for choosing the beneficial features while the ELM is employed to assess the selected features.

• Extensive experiments are implemented to evaluate the proposed approach using different performance metrics. In addition, to verify the efficiency of proposed model, the comparison is achieved with some related existing methods such as CFS + SVM [20], CFS + ANN [19], GA + Bagging [18], CFS + RF [30], and Chi-squared + RF [30].

摘要

•A hybrid Machine Learning (ML) approach for an efficient wrapper feature selection is proposed for enhancing the performance and reducing the computation time of IDS while increasing the accuracy to detect the intruder in IoT networks.•The hybrid ML method consists of two techniques: Differential Evolution (DE) and Extreme Learning Machine (ELM). The DE is responsible for choosing the beneficial features while the ELM is employed to assess the selected features.•Extensive experiments are implemented to evaluate the proposed approach using different performance metrics. In addition, to verify the efficiency of proposed model, the comparison is achieved with some related existing methods such as CFS + SVM [20], CFS + ANN [19], GA + Bagging [18], CFS + RF [30], and Chi-squared + RF [30].

论文关键词：Intrusion detection system (IDS),Feature selection,Differential evolution (DE),Extreme learning machine (ELM),NSL-KDD

论文评审过程：Received 19 August 2021, Revised 6 April 2022, Accepted 18 July 2022, Available online 21 July 2022, Version of Record 27 July 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108912