Research on second distribution of real value detectors based on heuristic movement

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