An Algorithm of Identification Learning and Optimization for Detectors

In order to detect the types of abnormal database in intrusion detection and to solve the clustering problem of mature detectors,an algorithm of identification learning and optimization for detectors is proposed.Firstly,each initialization mature detector is clustered as the center of non-self antigens to obtain the label of every detector in class,which identifies the types of abnormal database.Then,variation and deleting are operated on the overlapping detectors which are in the set of mature detectors to improve the coverage of non-self space after N-generation. Experimental results show that the proposed algorithm is able to improve the detection performance of the detectors as well as indicating the type of abnormal database.