Research of Network Intrusion Detection Model Based on Artificial Immune
基于人工免疫的网络入侵检测模型的研究

Aiming at limitation of existent network intrusion detection model with artificial immune idea, an improved network intrusion detection model based on dynamic clonal selection algorithm was presented. For accelerating normal IP packets access, the self-pattern class was proposed and most of self-antigens were filtered and amended the self-antigen set dynamically in detection process. The constraint based detectors were adopted as antibody, any-r intervals mat ching rule was used to determinant antibody and antigen, and split detector method were settled to self-antigen mat ching. The experimental results show that the proposed model can achieve a faster running speed, the better detecting rates,and adapt to dynamically changing environments.