Abstract: The detection rate of the traditional intrusion detection clustering algorithm is low. We combined the idea of semi-supervised learning and proposed a semi-supervised clustering algorithm oriented intrusion detection in order to improve it. Based on the part of the labeled data in the sample dataset, we generated the Seed set for initializing the cluster. The accuracy recognition of the intrusion detection data was achieved by calculating the Euclidean distance between the labeled data in the sample dataset and the average value of labeled data in each cluster and getting the initial center point. The blindness and randomness of the traditional cluster algorithm were avoided when choosing the initial center point. Furthermore, the efficiency of the detection was also improved. Experimental results showed that the proposed algorithm could utilize less label information via semi-supervised learning, and could achieve a higher efficiency than the traditional K-means method when dealing with intrusion detection dataset.

Key words: semi-supervised learning, semi-supervised clustering, intrusion detection, K-means, detection rate