ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features
作者：
Highlights：
• A selective ensemble of KELMs-based intrusion detection method is proposed.
• The KELM is introduced for the lightweight base classifier learning.
• Random projection is adopted for the feature representation of network instances.
• An incremental-learning-based online KELM updating approach is derived.
• A margin distance minimization-based selective ensemble method is presented.
摘要
•A selective ensemble of KELMs-based intrusion detection method is proposed.•The KELM is introduced for the lightweight base classifier learning.•Random projection is adopted for the feature representation of network instances.•An incremental-learning-based online KELM updating approach is derived.•A margin distance minimization-based selective ensemble method is presented.
论文关键词：Intrusion detection system,Random projection,Kernel extreme learning machine,Ensemble learning,Classifier selection
论文评审过程：Received 24 August 2018, Revised 11 April 2019, Accepted 13 April 2019, Available online 20 April 2019, Version of Record 22 May 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.04.008
