摘要
近年来DDoS攻击检测多采用机器学习的方法,Stacking便是其一,现阶段Stacking初级学习器的配置方法多为固定搭配,但由于DDoS攻击的复杂性和动态性,静态的配置策略显得灵活性较差。对此提出QGA-Stacking算法,即利用量子遗传算法(QGA)动态地选取Stacking中评价指标最高的一组学习器组合,从而提高检测模型的准确性和灵活性;提出一组最佳特征集来节省计算成本。经过实验对比,充分证明了QGA-Stacking算法相较于其他3种主流算法,其检测性能更加显著,最佳特征集的选取也较为合理。
In recent years,DDoS attack detection has mostly adopted machine learning methods,and Stacking is one of them.The current stacking base-learner configuration method is mostly fixed collocation.Due to the complexity and dynamics of DDoS attacks,static configuration strategy is obviously less flexible.In this regard,the QGA-Stacking algorithm is proposed,which uses quantum genetic algorithm(QGA)to dynamically select a group of learner combinations with the highest evaluation index in Stacking,thereby improving the accuracy and flexibility of the detection model.At the same time,a set of optimal feature sets was proposed to save computational cost.Through experimental comparison,it is fully proved that the QGA-Stacking algorithm has more significant detection performance than the other three mainstream algorithms,and the selection of the best feature set is more reasonable.
作者
付国庆
李俭兵
高雨薇
Fu Guoqing;Li Jianbing;Gao Yuwei(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Research Center of New Telecommunication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Information Technology Designing Co.,Ltd.,Chongqing 401121,China)
出处
《计算机应用与软件》
北大核心
2024年第3期321-327,共7页
Computer Applications and Software
基金
重庆市教育委员会科学技术研究项目(KJQN202000647)。
