An anomaly intrusion detection algorithm based on semi-supervised clustering
一种半聚类的异常入侵检测算法

An anomaly intrusion detection algorithm based on semi-supervised clustering along with k-nearest neighbor was presented. It could solve the problem of the insufficiency of training samples that the intrusion detection algorithms based on supervised learning face. The algorithm exploited minimal labeled data to improve its learning capability, and novelty detection could also be carried out. The experiment results manifest that the detection results of the algorithm precedes the one based on unsupervised learning remarkably, and approaches the one based on supervised learning while the labeled data are few.