An Intrusion Detection Method Based on Improved Neural Network

摘要

In order to solve the high false alarm rate problem in anomaly intrusion detection, an intrusion detection model of improved genetic algorithm based RBF neural network is put forward in this paper. In the model, cluster rule set is established through data mining methods and RBF neural network is optimized through improved GA. The suspicious behavior unmatched with any of the rules is detected through trained RBF neural network, and the specific types of intrusion can be identified. Experiments show that the model achieves better accuracy.