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面向超大容量光纤通信网络的安全域划分方法

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超大容量光纤通信网络节点分布较为混乱，节点间关联性复杂，导致安全域划分精准度低，严重威胁光纤通信网络的安全性。为此，提出一种面向超大容量光纤通信网络的安全域划分方法。根据光纤网络环境，采用K-means算法设定初始安全中心值，计算每个区域内代表样本值与该值之间距离，求解最大距离和最短距离平均标准值。在此基础上，采用粒子群分离法识别入侵信号，并分别计算在正常和存在入侵粒子干扰的情况下，各节点与设定局域间的隶属度值变化情况，以此确定信号相位差，根据节点间关联度数值划分符合同异反向量的节点区域，完成安全域划分。实验结果表明，所提方法划分精准度高，算法实施后节点参与攻击事件次数明显下降，网络安全得到强化。
Security domain partition method for ultra-large capacity optical fiber communication network
The distribution of nodes in ultra large capacity fiber optic communication networks is relatively chaot-ic,and the correlation between nodes is complex,resulting in low accuracy in security domain division,seriously threatening the security of fiber optic communication networks.To this end,a security domain partitioning method for ultra large capacity fiber optic communication networks is proposed.According to the fiber optic network environment,the K-means algorithm is used to set the initial security center value,calculate the distance between the representative sample value in each region and this value,and solve for the average standard values of the maximum distance and the shortest distance.On this basis,the particle swarm separation method is used to identify intrusion signals,and the changes in membership values between each node and the set local area are calculated separately under normal and presence of intrusion particle interference,in order to determine the phase difference of the signal.Based on the corre-lation value between nodes,the node area that matches the dissimilarity inverse vector is divided to complete the secur-ity domain division.The experimental results show that the proposed method has high accuracy in partitioning,and af-ter the implementation of the algorithm,the number of nodes participating in attack events significantly decreases,and network security is strengthened.

fiber optic communication networksecurity domain divisionK-means algorithmparticle swarm separation method

张志华、侯晓磊、张君君

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郑州工业应用技术学院信息工程学院,郑州 451150

光纤通信网络 安全域划分 K-means算法 粒子群分离法

教育部产学合作协同育人项目教育部产学合作协同育人项目河南省高等学校重点科研项目郑州市智能交通视频图像感知与识别重点实验室项目

20210225800220210225600823B790008郑科[2020]34号

2024

10.14016/j.cnki.jgzz.2024.06.190

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子：0.74
ISSN：0253-2743
年,卷(期)：2024.45(6)
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