Research On Hierarchical Anomaly Detection Based On Immunology

As a new intellective and information processing system, biological immune system has many excellent characteristics such as immune recognition, immune memory, self-adaptability and so on. So its research is great important. Recently anomaly detection based on immunology as a new method becomes very hot. In this paper, we research and improv the primal artificial immune model which Kim proposed, tod attaind some achievements.First, we analyze the characteristic and limitation of Kim artificial immune model. Then we introduce a multi-layer idea, and give a hierarchical model of anomaly detection based on immunology which is proposed first here. Futhermore we use mathematical symbol to abstract this new model. And this model can detect various anomaly layer by layer in order to improve the efficiency of detection.Second, we propose that mis new model may be looked as an improvement of immune algorithm. Immune algorithm is superior genetic algorithm by variety and memory mechanism, while the improvement of immune algorithm is superior immune algorithm by negative selection and hierarchical idea. This new algorithm can deal with the problem of precocity due to negative selection, and it also can detect various anomaly due to layered technology.Third, we propose a kind of method that evaluate the quality of algorithm by introducing index of clustering as the basis of evaluation.At last, we prove the validity of new method by experiment. And we mainly do two kind of experiment: one is to solve max. or min. of multi-peak-value function; the other is to solve all local extrema of multi-peak-value function.