An Intrusion Detection System Based on Support Vector Machine
基于支持向量机的入侵检测系统

The generalizing ability of current IDS (intrusion detection system) is poor when given less priori knowledge. Utilizing SVM (support vector machines) in Intrusion Detection, the generalizing ability of IDS is still good when the sample size is small (less priori knowledge). First, the research progress of intrusion detection is recalled and algorithm of support vector machine taxonomy is introduced. Then the model of an Intrusion Detection System based on support vector machine is presented. An example using system call trace data, which is usually used in intrusion detection, is given to illustrate the performance of this model. Finally, comparison of detection ability between the above detection method and others is given. It is found that the IDS based on SVM needs less priori knowledge than other methods and can shorten the training time under the same detection performance condition.