An Anomaly Intrusion Detection Technique of Support Vector Machine Based on Rough Set Attribute Reduction
一种基于粗糙集属性约简的支持向量异常入侵检测方法

This paper presented the implementation of a hybird anomaly intrusion detection technique based on rough set attribute reduction and support vector machine(SVM). According to the high dimension of network records with feature attributes,the rough set attribute reduction approach is firstly utilized to reducing data space and then the v-SVM algorithm is introduced into processing normalized data set. Experiments on DARPA 1998 data set show that the proposed anomaly detection technique achieves a comparable precise detection rate as the v-SVM algorithm based on all feature attributes,however,evidently decreases detection time as well as storage space.