基于萤火虫群优化支持向量机的网络入侵检测方法

Network Intrusion Detection Based on Glowworm Swarm Optimization SVM

为了解决支持向量机(SVM)方法应用于网络入侵检测中存在的参数设置和由样本不均带来的分类面偏斜问题,文章提出了一种基于萤火虫群优化支持向量机(GSO-SVM)的解决方案;算法在进行参数寻优的同时增加了修正因子,实现对分类面的修正,并采用萤火虫群算法进行求解,提高算法跳出局部最优的能力;在DARPA数据集上的检测精度达到97.33%,优于SVM和SVDD方法;实验结果表明文章提出的方法能够有效提高入侵检测模型的泛化性,降低误报率和漏报率。

The parameters setting and deflective of classification hyperplane caused by imbalance data affect the SVM＇s application in network intrusion detection.To resolve these problems,a new method based on glowworm swarm optimization SVM（GSO-SVM）was proposed.The algorithm optimizes SVM training parameters,and a modification gene was proposed to modify the classification hyperplane as well.The glowworm swarm optimization method was introduced to this optimization problem because of its excellent ability of escape locally optimal solution.The intrusion detection rate reaches 97.33%,higher than SVM and SVDD method.The experiments results show that GSO-SVM can improve the SVM＇s generalization and get lower false alarm rate and failed reporting rate.