Network intrusion detection is a detection technology which is based on the characteristics of network behavior. In recent years, network intrusion detection, as a research focus in the field of information security, had a rapid development. However, traditional intrusion detection algorithms run slow to extract feature. For this problem, this paper proposed the information entropy theory based on immune algorithm to improve the speed of feature extraction. In order to further improve the classification accuracy, the paper has been improved the Adaboost method to recognize the noise data in the classification process and to modify its weight, which could alleviate the Adaboost’s overfitting. The experimental results on the KDD CUP 99 showed that this method could speed up the convergence of immune algorithm and improve the intrusion detection rates effectively.
