基于灰狼算法优化核极限学习机的网络入侵检测研究

Research on Optimization of Network Intrusion Detection for Kernel Extreme Learning Machine Based on Grey Wolf Algorithm

针对核极限学习机的网络入侵检测结果易受正则化系数C和核参数g的影响,提出了一种灰狼算法优化KELM的网络入侵检测算法,提高KELM的网络入侵检测效果。实验结果表明,在检测率和误判率指标上,GWO_KELM算法的网络入侵检测率平均高达97.35%,优于其他算法。

The kernel learning machine may suffer from effect of parameters C and g when it is detected for intrusion. Therefore, the grey wolf algorithm is proposed to optimize KELM’s network intrusion detection algorithm in order to improve the effect of KELM. The experimental results show that the GWO_KELM algorithm has an average intrusion detection rate of 97.35 %, which is superior to other algorithms.