Research of immune intrusion detection algorithm based on semi-supervised clustering

Authors:
Xiaowei Wang
Affiliations:
Modern Education Technology Center, Physical Education College of Zhengzhou University, Zhengzhou, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Year:
2011

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Abstract

Traditional immune intrusion detection algorithms need lots of labeled training data. However, it is difficult to obtain sufficient labeled data in real situation. In this paper we present a semi-supervised clustering based immune intrusion detection algorithm called SCIID, which can improve the quality of antibodies constantly and enhance the detection rate. Experimental results show that SCIID can get the classes of most unlabeled data in the case of only having a few labeled data, and it can also discover new types of attacks. The detection rate of SCIID is higher than that of simply immune-based approach with the same number of training data.