Study of Fast Clustering Algorithm Based on Foregone Samples in Intrusion Detections

A fast clustering algorithm based on foregone samples for mixed data (FCABFS) in network anomaly detections technology is proposed in this paper. Original clustering center is exactly obtained by FCABFS through training foregone samples; Clustering center and non- similarity is calculated by separating objects. This algorithm solved problem of the higher false positive rate and the lower detection rate caused by using traditional clustering method with random selecting original clustering center and computing single attribute(continual or discrete) only in network anomaly detection. The experimental results compared with traditional clustering algorithm show that the detection rate is promoted 30%, and the false positive rate is diminished 25%. This algorithm can also obtain detections to new type attack through the method of unsupervised learning.