Title: Anomaly detection based on feature selection and multi-class support vector machines
Abstract:The most of Intrusion detection systems divided data into two classes, which were normal and abnormal, so that it might lose some important information.The goal of feature selection was to decrease th...The most of Intrusion detection systems divided data into two classes, which were normal and abnormal, so that it might lose some important information.The goal of feature selection was to decrease the redundant features for anomaly detection, and maintain the same high accuracy as the original features.It proposed an anomaly intrusion detection technique based on feature selection and multi-class support vector machines(SVM).The feature selection method merged RS, SVDF, LGP and MARS.Then, data was divided into five classes by the multi-class SVM.The experimental results demonstrate that the false positive rate of DoS is the highest one among four methods.Read More
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot