Network Security Based on Improved Genetic Algorithm and Weighted Error Back-Propagation Algorithm

In order to solve the problem of feature selection and local optimal solution in the field of network security, a network security protection model based on improved genetic algorithm and weighted error back-propagation algorithm is proposed. The model combines the dynamic error weight and adaptive learning rate of the weighted error back-propagation algorithm to improve the learning ability of the model in dealing with classification imbalance and dynamic attack mode. In addition, the global search capability of genetic algorithm is utilized to optimize the feature selection process and automatically adjust the hyperparameter settings. The experimental results show that the proposed model has an average accuracy of 96.7%, a recall rate of 93.3% and an F1 value of 0.91 on the CIC-IDS-2017 dataset, which has significant advantages over traditional detection methods. In many experiments, the accuracy of normal data is up to 99.97%, the accuracy of known abnormal behavior data is 99.31%, and the accuracy of unknown abnormal behavior data is 98.13%. These results show that this method has high efficiency and reliability when dealing with complex network traffic, and provides a new idea and method for network security protection research. © (2024), (Science and Information Organization). All Rights Reserved.