%0 Journal Article
%T Immunodominance-based Clonal Network Clustering Algorithm for Intrusion Detection<br>基于免疫优势克隆网络聚类的入侵检测
%A BAI Lin
%A <br>白琳
%J 计算机科学
%D 2012
%I 
%X According to the idea of intelligent complementary fusion, a combination of immunodominance, inverse operation, clonal selection, non-uniform mutation and forbidden clone was employed in a novel clustering method with network structure for intrusion detection. The clustering process was adjusted in accordance with affinity function and evolution strategics. So an intelligent, self-adaptive and self-learning network was `evolved' to reflect the distribution of original data. Then the minimal spanning tree was employed to perform clustering analysis and obtain the classification of normal and anormal data. I}he simulations through the KDD CUP99 dataset show that the novel method can deal with massive unlabeled data to distinguish normal case and anomaly and even can detect unknown intrusions effectively.
%K Immunodominance
%K Non-uniform mutation
%K Clonal selection
%K Forbidden clone
%K Evolutionary network
%K Intrution dctctlion<br>免疫优势
%K 非一致性变异
%K 克隆选择
%K 禁忌克隆
%K 进化网络
%K 入侵检测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=4BC43EF34E35AF52787CA04775A378A5&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=DF92D298D3FF1E6E&sid=0D0D661F0B316AD5&eid=4978C66F2CE32152&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0