%0 Journal Article
%T Intrusion Detection Based on Clustering and Unlabeled Data<br>基于聚类的未标识数据的入侵检测*
%A LIU Shu-jie
%A DING Hong
%A <br>刘术杰
%A 丁宏
%J 计算机应用研究
%D 2005
%I 
%X Automatical Intrusion Detection System is becoming more and more important in the area of Intrusion Detection System(IDS). Traditional IDS's which rely on labeled datas to train ,can't update the rules and detect intrusions automatically. This paper presents a frame work for automatically detecting intrusions:intrusion detection based on clustering and unlabeled data. It doesn't rely on labeled datas to train and can detect the new intrusions keeping low false positive rate.
%K Intrusion Detection
%K Clustering
%K Pecentage of the Largest Clusters<br>入侵检测
%K 聚类
%K 标识比例
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=FAD89F6730BD7079&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=9CF7A0430CBB2DFD&sid=B0EBA60720995721&eid=A8DE7703CC9E390F&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=6