中文摘要：

支持向量机作为一种优良的分类算法应用在网络入侵检测系统中，但是训练时间过长是它的主要缺陷．文中提出了基于量子粒子群优化的属性约简和支持向量机（SVM）的入侵检测方法，利用量子粒子群优化的属性约简算法对训练样本集进行属性约简，剔除了对入侵检测结果影响较小的冗余特征，从而使入侵检测系统在获取用户特征的时间减少，整个入侵检测系统的性能得到提高．实验结果表明，该方法是有效的．

英文摘要：

The SVM is one of the most successful classification algorithms in network intrusion detection （NIDS） area, but its long training time limits its use. This paper presents a method for enhancing the training time of SVM using attribute reduction optimized by quantum - behaved particle swarm optimization （QPSO）, specifically when dealing with large training data sets in NIDS. The reduction algorithm based on attribute reduction optimized by QPSO is used to eliminate the redundant features of sample data set, with the attributes of the raw data are reduced, the SVM training time are reduced. The NIDS based on attribute reduction optimized by QPSO and SVM has better performance. Experimental results show that this method is efficient.