Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
作者：
Highlights：
• Reduction the 10%KDD training dataset up to 99.8% by using modified K-means.
• New high quality training datasets are constructed for training SVM and ELM.
• Multi-level model is proposed to improve the performance of detection accuracy.
• Improve the detection rate of DoS, U2R and R2L attacks.
• Overall accuracy of 95.75% is achieved with whole Corrected KDD dataset.
摘要
•Reduction the 10%KDD training dataset up to 99.8% by using modified K-means.•New high quality training datasets are constructed for training SVM and ELM.•Multi-level model is proposed to improve the performance of detection accuracy.•Improve the detection rate of DoS, U2R and R2L attacks.•Overall accuracy of 95.75% is achieved with whole Corrected KDD dataset.
论文关键词：Intrusion detection system,Support vector machine,Extreme learning machine,K-means,Multi-level,KDD Cup 1999
论文评审过程：Received 30 August 2015, Revised 29 September 2016, Accepted 30 September 2016, Available online 30 September 2016, Version of Record 7 October 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.041
