%0 Journal Article
%T A Supervised Local Decision Hierachical Support Vector Machine Based Anomaly Intrusion Detection Method<br>一种基于有监督局部决策分层支持向量机的异常检测方法
%A Xu Qin-zhen Yang Lü
%A -xi
%A <br>徐琴珍
%A 杨绿溪
%J 电子与信息学报
%D 2010
%I 
%X This paper dedicates to propose a supervised local decision Hierachical Support Vector Machine (HSVM) learning model for anomaly intrusion detection in high dimensional feature space. The binary-tree structure of HSVM presents a “divide-and-conquer” algorithm for complex anomaly intrusion detection problem, i.e., the training signal for supervising local decision at each internal node is constructed according to information gain criterion. The embedded SVMs at internal node are trained on local optimized feature subsets standing on the sensitivity degrees of a margin to features. The experimental results suggest that the proposed anomaly intrusion detection method can gain learning model with better stability under the local decision supervisal of training signals. Further, it also achieves competitive detection accuracy and higher detection efficiency with condensed feature information.
%K Anomaly intrusion detection
%K Hierachical Support Vector Machine (HSVM)
%K Feature credit
%K Supervised local decision<br>异常入侵检测
%K 分层支持向量机
%K 特征信用度
%K 有监督局部决策
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=A06D445AD9EB038E9042C159B988581D&yid=140ECF96957D60B2&vid=9971A5E270697F23&iid=F3090AE9B60B7ED1&sid=76CE0C8B87EE0D79&eid=44DA216FA1E0217E&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=14