为了去除冗余特征，降低系统存储和运算负担，提高网络入侵检测分类器的性能，文中提出了一种基于Fisher分和支持向量机的网络入侵检测特征提取方法．针对KDD^,99网络入侵检测数据集，应用该方法得到了混合攻击和4种单一攻击模式下的特征重要度排序，选取重要特征建立支持向量机入侵检测分类器．结果表明，该分类器精度与使用全部特征构建的支持向量机分类器相当，训练和测试时间则显著降低

In order to eliminate redundant features, reduce the system burden of storage and computation, and improve the performance of the classifier for network intrusion detection, a method to extract network intrusion detection feature is proposed based on the Fisher score and the support vector machine （SVM）. Then, in accordance with KDD^,99 network intrusion detection dataset, the feature significance rankings for the mixed attack and four single attacks are respectively obtained by using the proposed method. By extracting important features, a SVM classifier is thus constructed. Experimental results show that, as compared with the classifier constructed based on all features, the new classifier is of approximately equivalent accuracy and dramatically low training and testing time cost.