%0 Journal Article
%T Application of Online SVM in real-time intrusion detection<br>Online SVM在实时入侵检测中的应用研究
%A LI Heng-jie
%A <br>李恒杰
%J 计算机应用
%D 2007
%I 
%X As a new classification method, Online Support Vector Machines (Online SVM) can offer superior classification performance for anomaly intrusion detection. The conventional SVM, Robust SVM and one-class SVM have been modified respectively based on the idea from Online SVM in this paper, and their performances have been compared with that of the original algorithms. After elaborate theoretical analysis, concrete experiments with 1999 DARPA data set collected at MIT's Lincoln Labs were carried out. These experiments verify that the modified SVMs can be trained online and the results outperform the original ones with fewer Support Vectors (SVs) and less training time without decreasing detection accuracy in the presence of noise.
%K intrusion detection system
%K anomaly detection
%K Support Vector Machines (SVM)
%K noisy data
%K online training<br>入侵检测系统
%K 异常检测
%K 支持向量机
%K 噪声数据
%K 在线训练
%K Online
%K 实时入侵检测
%K 应用
%K 研究
%K intrusion
%K detection
%K 程度
%K 虚警率
%K 检测正确率
%K 情况
%K 存在
%K 噪声数据
%K 训练时间
%K 支持向量
%K 在线训练
%K 发现
%K 实验
%K 评估数据
%K DARPA
%K 使用
%K 比较
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD274305128CFA63596DB56D44&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=59D5B0EDB2D8DABC&eid=E3C11E2483CABC48&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=9