一种基于MetaCost和RF的网络入侵检测方法分析
Analysis of a Network Intrusion Detection Method Based on MetaCost and Random Forest
王雄伟 1张鑫楠1
作者信息
- 1. 曹妃甸职业技术学院人工智能学院,河北 063200
- 折叠
摘要
阐述一种基于MetaCost-RF的网络入侵检测算法,该算法在RF训练过程中通过引入代价矩阵来减小不平衡数据集给RF带来的负面影响.在NSL-KDD上对训练好的模型进行测试验证,结果表明,MetaCost-RF对比RF在准确率上提高5.16个百分点,在三个少数类的召回率上分别提高了 10.82、20.00和21.17个百分点,说明该模型有效提高了准确率和对少数类样本的召回率.
Abstract
This paper describes a network intrusion detection algorithm based on MetaCost-RF by combining the cost-sensitive learning method.The algorithm reduces the negative impact of unbalanced datasets on RF by introducing a cost matrix during RF training.Test validation of trained models on NSL-KDD.The results show that the accuracy of MetaCost-RF is improved by 5.16 percentage points compared with RF,and the recall rate of three minority classes is improved by 10.82,20.00 and 21.17 percentage points respectively.It is shown that the model is effective in enhancing the accuracy.Besides,it improving recall for a small number of classes of samples.
关键词
不平衡数据集/MetaCost/随机森林/网络入侵检测/代价矩阵Key words
unbalanced datasets/MetaCost/random forest/network intrusion detection/cost matrix引用本文复制引用
出版年
2024
NETL
NSTL
万方数据