Abstract:For addressing the issue of unauthorized actions bypassing security mechanisms to attack systems in the integrated network of heaven and earth in the open electromagnetic environments, an improved Genetic Algorithm(GA) is proposed. It uses the Decision Tree(DT) algorithm as the fitness function, and significantly improves the interception rate of network attacks by deleting redundant features in the dataset.Anomaly classification is performed through machine learning, and the feature selection function of the genetic algorithm is employed to enhance the classification efficiency of machine learning. To verify the effectiveness of the proposed algorithm, the UNSW_NB15 and UGRansome1819 datasets are selected for training and testing. Four machine learning classifiers, namely Random Forest(RF), Artificial Neural Network(ANN), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM), are used for evaluation. The performance of the algorithm is evaluated through indicators such as accuracy, F1 score, recall rate, and confusion matrix. The experiment results prove that the genetic algorithm as a feature selection tool can significantly improve the classification accuracy and achieve significant improvement in algorithm performance. Meanwhile, to tackle with the instability of weak classifiers, this paper further proposes an ensemble learning optimization technique, which integrates weak classifiers and strong classifiers for optimization. The experiment confirms the excellent performance of this optimization algorithm in improving the stability of weak classifiers.