Abstract: Abstract： In order to enhance the accuracy and practicability of the traditional network intrusion detection model， this paper proposes a network intrusion detection based on an improved gradient lift tree （XGBoost） model. Firstly， the random forest algorithm is used to predict the key feature points， and the feature with the highest importance weight is effectively selected and the feature set is constructed in the data pre-processing stage. Secondly， the prediction method of XGBoost model is improved by using card equation. Finally， the cost sensitive function is introduced into the XGBoost optimization algorithm to improve the detection rate of small sample data， and the mesh method is used to reduce the complexity of the model. Experimental simulation results show that compared with other artificial intelligence algorithms， the proposed model can reduce the waiting time by more than 50% with higher inspection accuracy， and has strong scalability and adaptability under noisy environment. Combined with other models， the experimental results show that the tree depth has the greatest impact on the model performance.

Key words: Key words： invasion detection, feature selection, random forest, XGBoost, cost sensitive function