摘要
提出了一个基于增量学习支持向量机的DoS入侵检测方法,其基本思想是将训练样本库分割成几个互不相交的训练子库,按批次对各个训练子库样本进行训练,每次训练中只保留支持向量,去除非支持向量。与传统的基于支持向量机的入侵检测方法对比的试验表明,该方法在不影响检测性能的同时明显减少了训练时间。
This paper proposes a novel method for DoS intrusion detection based on incremental learning with SVM whose main idea is to segment the training database which is composed of log files into sub-databases which are mutually exclusive each other, and each sub-database is trained in batch. During each training process, only support vector is reserved for future training and non-support-vector is discarded. Compared with the method based on traditional SVMs, this training algorithm obviously reduces training time and obtains high detection performance.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第4期179-180,186,共3页
Computer Engineering
基金
浙江省自然科学基金资助项目(601110)
关键词
入侵检测
拒绝服务
增量学习
支持向量机
Intrusion detection
Denial of service（DoS）
Incremental learning
Support vector machine
作者简介
刘晔（1980-），男，硕士生，主研方向：网络与信息安全，软件工程。E-mail：liuye12l@21cn．com
王泽兵，教授。
冯雁，副教授。
占红英，讲师、博士生。
