New Method for Intrusion Detection Based on BPNN and Improved GA Optimization

It's very important to detect the network attacks to protect the information security. The intrusion patter identification is a hot topic and using the artificial neural networks (ANN) to provide intelligent intrusion recognition has been received a lot of attentions. However, the intrusion detection rate is often affected by the structure parameters of the ANN. Improper ANN model design may result in a low detection precision. To overcome these problems, a new network intrusion detection approach based on improved genetic algorithm (GA) and BPNN classifiers is proposed in this paper. The improved GA used energy entropy to select individuals to optimize the training procedure of the BPNN, and satisfactory BPNN model with proper structure parameters. The efficiency of the proposed method was evaluated with the practical data. The experiment results show that the proposed approach offers a good intrusion detection rate, and performs better than the standard GA-BPNN method.