基于随机森林和XGBoost的网络入侵检测模型

Network Intrusion Detection Model Based on Random Forest and XGBoost

为提高复杂网络环境中入侵检测模型的准确性和实时性,提出一种基于随机森林和极端梯度提升树(XGBoost)的网络入侵检测模型RF-XGB。首先针对随机森林算法计算特征重要性的特点,设计混合特征选择方法高效筛选出最有价值的特征子集;在XGBoost算法中引入代价敏感函数来提高对少样本类别的检测率,使用网格法调参降低模型复杂度。实验仿真结果表明,与其他机器学习算法相比,所提出的模型在具备更高检测精度的情况下减少了50%以上的处理时间,并在噪声影响下具有较好的鲁棒性和自适应性。

To improve the accuracy and real-time performance of intrusion detection models in complex network environments,a network intrusion detection model based on random forest and eXtreme Gradient Boosting(XGBoost)is proposed.First,the feature importance is calculated based on the random forest algorithm.A hybrid feature selection method combining filtering and embedded is used to reduce the feature dimension of the dataset.When detecting the sample category,the XGBoost algorithm based on cost-sensitive function and grid method tuning is used to improve Model accuracy.Experimental simulation results show that compared with other machine learning algorithms,the proposed model greatly reduces processing time by more than 50% with higher detection accuracy,and has better robustness and adaptability.