A method of SVM with Normalization in Intrusion Detection

Network intrusion is always hidden in a mass of routine data and the differences between these data are very large. Normalization can help to speed up the learning phase and avoiding numerical problems such as precision loss from arithmetic overflows. Some normalization methods are analyzed and simulated. Experiments results show that the method using SVM with normalization has much better performance compared to the method using SVM without normalization in classing intrusion data of KDD99 and Min-Max Normalization has better performance in speed, accuracy of cross validation and quantity of support vectors than other normalization methods.