基于特征选择的无监督入侵检测方法

为提高入侵检测系统的检测速度和效果，结合遗传算法提出了一种基于特征选择的无监督入侵检测方法。一方面利用改进的遗传算法作为搜索策略;一方面使用K均值聚类算法对提取特征后的数据进行聚类，并将类间离散度和类内离散度的相关比值作为特征子集的评价指标，从而实现最优特征子集的求解并用于无监督的入侵检测。实验结果表明，该方法由于解决了入侵检测的特征选择问题，与未采用特征选择的无监督入侵检测相比具有更好的性能。

Unsupervised intrusion detection based on feature selection

In order to improve performances of intrusion detection system in terms of detection speed and detection rate,a novel unsupervised intrusion detection method based on Genetic Algorithm(GA) and feature selection mechanism is proposed.In the method,an improved GA is adopted as search strategy.On the other hand,the K-means clustering is used to classify the feature data,whose evaluation target is the ratio of the between-class scatter to the within-class scatter.Then,the optimal feature subset is found and app...