基于增量支持向量机的DoS入侵检测

DoS Intrusion Detection Based on Incremental Learning with Support Vector Machines

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.