基于PSOGWO-SVM的网络入侵检测方法

针对SVM算法的核函数及参数选择不科学会导致检测的准确率比较差的问题，提出了一种融合粒子群搜索的灰狼优化算法，利用PSOGWO算法优化SVM的参数，确定SVM分类器的最优检测模型，并基于NSL KDD数据集进行对比实验。结果表明：基于PSOGWO SVM的入侵检测方法实现了SVM的参数最优化，而且在检测率、收敛速度、模型平衡性等方面有明显提升，该方法在网络入侵检测方面具有更好的性能。

A Network Intrusion Detection Method Based on PSOGWO-SVM

Aimed at the problems that the selection of kernel function and parameter adjustment in SVM algorithm are not scientific, and the detection is poor in accuracy of classification, a Grey Wolf Optimization Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm is proposed to improve the Intrusion Detection System (IDS) based on SVM. This method is to utilize PSOGWO algorithm for optimizing the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The optimal detection model of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments are made based on NSL-KDD dataset, and the results show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, improving significantly the detection rate, the convergence speed and the model balance. And this algorithm is feasible.