Novel intrusion detection algorithm based on semi-supervised clustering
一种新的半监督入侵检测算法

An anomaly intrusion detection algorithm based on semi-supervised clustering along with PSO K-means was presented. It could solve the problems of the low detection rate of the intrusion detection algorithms based on unsupervised learning, and the insufficiency of training samples of the intrusion detection algorithms based on supervised learning. The algorithm utilized minimal labeled data and lots of unlabeled data to improve its learning capability, and novelty detection could also be carried out. The experimental results manifest that the detection results of the algorithm outperforms both the one based on unsupervised learning remarkably and the one based on supervised learning.