摘要
为解决传统计算机网络入侵节点选择算法存在选择准确性差,工作效率低的问题,本文提出研究基于点群聚类的计算机网络入侵节点选择算法。该算法首先进行入侵节点数据收集,对获取到的数据进行清洗、归约、转换等处理。利用点群聚类中的K-means算法识别入侵节点,实现了计算机网络入侵节点选择。仿真结果表明:通过本文算法对计算机网络入侵节点进行选择的综合性能TotalScore值较高,用时较短,证明本算法能在更短的时间内实现网络入侵节点选择,更利于预防非法节点入侵,保护网络安全。
In order to solve the problems of poor selection accuracy and low work efficiency in the traditional computer network intrusion node selection algorithm,this paper proposes a computer network intrusion node selection algorithm based on point clustering.The algorithm first carries on the intrusion node data collection,carries on the cleaning,the reduction,the transformation and so on to the obtained data processing.The K-means algorithm in point clustering is used to identify the intrusion nodes,and the computer network intrusion node selection is realized.The simulation results show that the comprehensive performance of the proposed algorithm for the selection of computer network intrusion nodes is higher and the time is shorter,which proves that the algorithm can be more effective in the selection of computer network intrusion nodes.The network intrusion node selection is realized in a short time,which is more beneficial to prevent illegal node intrusion and protect network security.
作者
于府平
YU Fu-ping(Yantai Automobile Engineering Professional College,Yantai 265500,China)
出处
《电子设计工程》
2020年第17期42-45,51,共5页
Electronic Design Engineering
基金
山东省科技厅项目基金(60674091)
校级科研课题(2018LXB0123)
校级科研课题(16SB009)。
关键词
点群聚类
计算机网络
入侵节点
选择算法
point cluster
computer network
intrusion node
selection algorithm
