Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune de...Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.展开更多
在入侵检测系统中引入图论的相关理论并提出了一种基于图论的入侵检测方法,将数据对象之间相似度的关系转换到图论的邻接矩阵中,再将邻接矩阵转换为关联矩阵,以表示数据对象之间的相似关系.利用最速下降法求得最佳的转换矩阵,以完成关...在入侵检测系统中引入图论的相关理论并提出了一种基于图论的入侵检测方法,将数据对象之间相似度的关系转换到图论的邻接矩阵中,再将邻接矩阵转换为关联矩阵,以表示数据对象之间的相似关系.利用最速下降法求得最佳的转换矩阵,以完成关联矩阵的块对角矩阵转换而达到数据聚类效果和鉴别出正常数据与入侵攻击数据的类别.同时,利用KDD CUP 1999数据集对系统进行仿真.结果表明,所提出的入侵检测方法能够在很低误警率的情况下达到比模糊C均值聚类算法更高的检测率.展开更多
基金This research was funded by the Scientific Research Project of Leshan Normal University(No.2022SSDX002)the Scientific Plan Project of Leshan(No.22NZD012).
文摘Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.
文摘在入侵检测系统中引入图论的相关理论并提出了一种基于图论的入侵检测方法,将数据对象之间相似度的关系转换到图论的邻接矩阵中,再将邻接矩阵转换为关联矩阵,以表示数据对象之间的相似关系.利用最速下降法求得最佳的转换矩阵,以完成关联矩阵的块对角矩阵转换而达到数据聚类效果和鉴别出正常数据与入侵攻击数据的类别.同时,利用KDD CUP 1999数据集对系统进行仿真.结果表明,所提出的入侵检测方法能够在很低误警率的情况下达到比模糊C均值聚类算法更高的检测率.
