DoS Intrusion Detection Based on Incremental Learning with Support Vector Machines

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.