摘要
提出了一种聚类学习与增量SVM训练相结合的的入侵检测方法,采用聚类分析、样本修剪与增量学习相结合的方式,通过聚合相似的训练样本以支持多类别分类,通过去除相似的样本而只取其代表点,从而减少参加训练的样本数量,提高学习效率,同时采用基于广义KKT判决的增量学习方法,有效改善了多类别入侵检测场合下样本数据集过于庞大,学习速度过慢且难以保障SVM入侵检测能力持续优化的问题。
An novel incremental SVM intrusion detection algorithm based on Clustering Learning is proposed in this paper. By using the clustring analysis and data pruning, the purpose of efficient simplification and multi-classification for trainning samples is achieved. An improved algorithm of incremental SVM trainning based on generalized KKT condition is also presented. Our simulation result shows that this method could effectively improve the trainning and classification speed caused by datasets updating,while at the same time the classification precision is guaranteed.
作者
王亚兵
WANG Ya-bin （Jiangsu Tcnet Technology Co., LTD., Suzhou 215000,China）
出处
《电脑知识与技术》
2014年第7期4417-4420,4432,共5页
Computer Knowledge and Technology
关键词
支持向量机
入侵检测
聚类
非线性分类
SVM
Intrusion Detection
Clustering
non-Linear Classification
