Network Intrusion Detection Based on Xgboost Model Improved by Quantum-behaved Particle Swarm Optimization

This paper proposes the method of optimizing the Xgboost model by Quantum-behaved Particle Swarm Optimization (QPSO), and validates its effectiveness in the context of network intrusion detection. We preprocess the data set first, and then use QPSO to optimize the parameters of the Xgboost model. After that, we use the test set to evaluate the optimized model. The experimental results show that the Xgboost model optimized by QPSO has good performance in terms of precision, recall rate and average precision, and is better than the unoptimized Xgboost model and grid search method.