A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine
作者：
Highlights：
• Extreme learning machine with hybrid kernel function (HKELM) approach is proposed.
• A hybrid of GSA and DE algorithm is employed to optimize the parameters of HKELM.
• The kernel principal component analysis (KPCA) is introduced for feature extraction.
• A novel intrusion detection approach KPCA-DEGSA-HKELM is obtained.
• The superiority of KPCA-DEGSA-HKELM is validated by intrusion detection problems.
摘要
•Extreme learning machine with hybrid kernel function (HKELM) approach is proposed.•A hybrid of GSA and DE algorithm is employed to optimize the parameters of HKELM.•The kernel principal component analysis (KPCA) is introduced for feature extraction.•A novel intrusion detection approach KPCA-DEGSA-HKELM is obtained.•The superiority of KPCA-DEGSA-HKELM is validated by intrusion detection problems.
论文关键词：Intrusion detection system,Extreme learning machine,Gravitational search algorithm,Differential evolution,Kernel principal component analysis
论文评审过程：Received 23 August 2019, Revised 7 February 2020, Accepted 10 February 2020, Available online 13 February 2020, Version of Record 4 April 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.105648
