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
针对现有人工免疫网络算法对先验知识应用不足的问题,提出一种基于模糊人工免疫网络的有监督学习数据分类方法．首先采用模糊C均值聚类算法为免疫网络提供疫苗(初始种群),将此疫苗作为免疫网络的初始抗体群,种群再经过克隆选择、网络压...针对现有人工免疫网络算法对先验知识应用不足的问题,提出一种基于模糊人工免疫网络的有监督学习数据分类方法．首先采用模糊C均值聚类算法为免疫网络提供疫苗(初始种群),将此疫苗作为免疫网络的初始抗体群,种群再经过克隆选择、网络压缩、免疫成熟、记忆等算子的不断扩展和压缩,形成一个由浓缩后的训练数据构成的抗体网络,最终基于该抗体网络采用“邻近原则”构造分类器．由于各算子的协调作用,该方法能够在高浓缩率的情况下更好地代替样本空间．UCI (University of Califomia,Irvine)数据集的仿真实验证明,与aiNet方法相比,该方法在分类准确率和数据浓缩率上分别高出7.26%和11.16%,而且更稳定、可靠．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.
文摘针对现有人工免疫网络算法对先验知识应用不足的问题,提出一种基于模糊人工免疫网络的有监督学习数据分类方法．首先采用模糊C均值聚类算法为免疫网络提供疫苗(初始种群),将此疫苗作为免疫网络的初始抗体群,种群再经过克隆选择、网络压缩、免疫成熟、记忆等算子的不断扩展和压缩,形成一个由浓缩后的训练数据构成的抗体网络,最终基于该抗体网络采用“邻近原则”构造分类器．由于各算子的协调作用,该方法能够在高浓缩率的情况下更好地代替样本空间．UCI (University of Califomia,Irvine)数据集的仿真实验证明,与aiNet方法相比,该方法在分类准确率和数据浓缩率上分别高出7.26%和11.16%,而且更稳定、可靠．
