中文摘要：

通过分析入侵检测样本的分布特点,提出了一种多分类SVM增量学习算法.该算法通过衡量同类样本点和样本中心之间的距离来确定用于训练的支持向量,以选择对分类贡献较大的边缘向量进行训练,通过求解多个超平面的方法划分出不同类别样本的区域,实现了多分类的增量学习.在保证检测率的同时,减少了样本学习数量.利用KDDCUP99标准数据集进行测试,证明该算法可以大幅度降低训练的时间和空间复杂度.

英文摘要：

This paper proposed a new algorithm of multi-category SVM incremental learning by analyzing the distribution characteristics of the intrusion detection data.Samples used in learning were selected by measuring the distance between sample points and their class-centers,and they are those samples which will most possibly be the SVs in incremental learning.By several binary-class hyper-planes,the zones of the inhomogeneous samples are divided,so the multi-category incremental learning is realized.Using this alg...