基于遗传算法和SMOTE的网络入侵检测模型优化研究
A study on improving the network intrusion detection model using genetic algorithm and SMOTE
戴周浩1
作者信息
- 1. 贵州财经大学信息学院,贵阳 550000
- 折叠
摘要
针对网络入侵检测存在数据不平衡和特征冗余的问题,提出一种新的检测模型.该模型结合了遗传算法和SMOTE算法,通过对数据进行采样和特征选择,提高了网络入侵检测的准确性.首先,为了解决数据不平衡的状况,采用了SMOTE算法.这个算法通过在入侵类样本中嵌入随机样本,有效地提高了入侵类样本的数量,使得数据达到平衡.其次,为了缓解特征冗余,引入了基于遗传算法和随机森林方法的包装式特征选择技术,选择有用特征,减少冗余信息,从而提升最终的入侵检测性能.最后,采用随机森林算法对经过上述处理的数据集进行分类,实现对网络入侵样本的有效检测.在NSL-KDD数据集上的实验表明,基于遗传算法和SMOTE的网络入侵检测模型从整体上提高了入侵检测的识别率.
Abstract
A new detection model is proposed to address the issues of data imbalance and feature redundancy in network intru-sion detection.The model integrates genetic algorithms and the Synthetic Minority Over-sampling Technique(SMOTE)to enhance the accuracy of network intrusion detection.Firstly,to address data imbalance,the SMOTE algorithm is employed,inserting ran-dom samples between minority class instances to effectively increase their quantity and achieve inter-class balance.Moreover,a wrapper feature selection process,based on genetic algorithms and random forests,has been established to reduce feature redun-dancy.This process not only picks useful features and lessens unnecessary data,but also enhances the overall efficiency of intru-sion detection.Finally,the processed dataset undergoes classification using the random forest algorithm,enabling effective detec-tion of network intrusion instances.Experiments on the NSL-KDD dataset show that the network intrusion detection model based on genetic algorithm and SMOTE improves the overall recognition rate of intrusion detection.
关键词
特征选择/SMOTE过采样/随机森林/网络入侵检测/遗传算法Key words
feature selection/SMOTE oversampling/random forest/network attack detection/genetic algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据