利用粒子群优化的极限学习机入侵检测算法

The algorithm of intrusion detection of kernel extreme learning machine using PSO

针对网络入侵检测准确率低、误报率高的状况,通过理论分析与仿真实验,提出一种利用粒子群优化的极限学习机入侵检测算法.该算法利用粒子群算法优化核极限学习机的核参数,采用学习能力和线性组合泛化能力强的全局性核函数,形成多核极限学习机,可以有效提高单核极限学习机分类器的性能.通过仿真实验对其性能进行了对比分析,结果验证了该算法的有效性.

Aiming at solving the problems of low accuracy and high false alarm rate for network intrusion detection,an algorithm of intrusion detection based on particle swarm optimization and kernel extreme learning is proposed.In this article,particle swarm optimization is used to optimize the kernel parameters of kernel limit learning machine.The global kernel function with strong generalization ability of linear combination and the local kernel function with strong learning ability are used to form multi-core limit learning machine,which can improve the performance of single core extreme learning machine classifier.Finally,the performance of the algorithm is compared and analyzed through experiments and the experimental results verify the validity of this algorithm.