Immunodominance-based Clonal Network Clustering Algorithm for Intrusion Detection
基于免疫优势克隆网络聚类的入侵检测

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.