Research on Loophole with Second Distribution of Real Value Detectors

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. There still remain many problems such as the randomness of detector generation, incompleteness of selfset and the generalization ability of detectors, which would cause a lot of loopholes in non-self space. A heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self space in the first distribution and the mutation regions of self space is proposed. The algorithm can distribute real value detectors through omission data based on the methods of partition and movement. A method is proposed to solve the problem on how to get the optimal solutions to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that our algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.