Network intrusion detection method based on feature selection and XGboost

Network Intrusion Detection (NID) plays a very important role in the field of network security. This method utilizes an information gain-based feature selection approach and applies XGBoost for classification, optimizing model parameters. By conducting feature selection and removing irrelevant features, the computational complexity is reduced, training speed is accelerated, and detection rates are improved. The method was validated using the UNSW-NB15 dataset and demonstrated higher detection rates and F1 scores compared to four other algorithms in the same field. It exhibits high precision, fast convergence speed, and strong generalization performance. Effectively resolving the issue of multi-class imbalance in network intrusion detection data, this method presents a feasible approach for network intrusion detection.