太赫兹科学与电子信息学报2024，Vol.22Issue(3)：249-260,12.DOI:10.11805/TKYDA2023393
基于改进遗传算法的入侵检测技术的设计与实现
Design and implementation of intrusion detection technology based on improved genetic algorithm
摘要
Abstract
For addressing the issue of unauthorized actions bypassing security mechanisms to attack systems in the integrated network of heaven and earth in the open electromagnetic environments,an improved Genetic Algorithm(GA)is proposed.It uses the Decision Tree(DT)algorithm as the fitness function,and significantly improves the interception rate of network attacks by deleting redundant features in the dataset.Anomaly classification is performed through machine learning,and the feature selection function of the genetic algorithm is employed to enhance the classification efficiency of machine learning.To verify the effectiveness of the proposed algorithm,the UNSW_NB15 and UGRansome1819 datasets are selected for training and testing.Four machine learning classifiers,namely Random Forest(RF),Artificial Neural Network(ANN),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM),are used for evaluation.The performance of the algorithm is evaluated through indicators such as accuracy,Fl score,recall rate,and confusion matrix.The experiment results prove that the genetic algorithm as a feature selection tool can significantly improve the classification accuracy and achieve significant improvement in algorithm performance.Meanwhile,to tackle with the instability of weak classifiers,this paper further proposes an ensemble learning optimization technique,which integrates weak classifiers and strong classifiers for optimization.The experiment confirms the excellent performance of this optimization algorithm in improving the stability of weak classifiers.关键词
机器学习/遗传算法/决策树/特征选择Key words
machine learning/Genetic Algorithm/Decision Tree/feature selection分类
信息技术与安全科学引用本文复制引用
王硕,李成杰,崔丽琪,李聪,乐秀权,戴志坚..基于改进遗传算法的入侵检测技术的设计与实现[J].太赫兹科学与电子信息学报,2024,22(3):249-260,12.基金项目
中央高校基本科研业务费专项基金优秀学生培养工程资助项目(2023NYXXS034) （2023NYXXS034）
基础加强资助项目(2020-JCJQ-ZD-119) （2020-JCJQ-ZD-119）
