arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.01587cs.ITmath.IT
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面向网络整体安全的拓扑编码和拓扑矩阵

Topological Coding and Topological Matrices Toward Network Overall Security

Bing Yao, Meimei Zhao, Xiaohui Zhang, Yarong Mu, Yirong Sun, Mingjun Zhang, Sihua Yang, Fei Ma, Jing Su, Xiaomin Wang, Hongyu Wang, Hui Sun

摘要:

矩阵的数学拓扑是编码关系结构的自然表示,在世界上的许多领域都可以找到。矩阵在实际应用的计算中是非常重要的,空间矩阵在计算机中存储方便,运算速度快,而且矩阵便于处理当前网络的群体,如Laplacian矩阵、图论中的邻接矩阵等。基于当今网络计算和研究中所使用的方便、有用和强大的矩阵,我们引入了Topcode矩阵,它是$3\times q$阶矩阵,不同于线性代数和计算机科学中常用的矩阵。Topcode矩阵可以使用数字、字母、汉字、集合、图形、代数群\emph{etc.}作为其元素。重要的一点是,Topcode矩阵的数字可以很容易地得出数字字符串,因为数字字符串是基于文本的密码用于信息安全。Topcode矩阵可以用来描述信息安全中的拓扑图密码(Topsnut-gpws)和图的连通性,以解决GoogleBrain和DeepMind提出的图网络和图神经网络研究中出现的一些问题。本文主要讨论Topsnut-矩阵、Topcode-矩阵、Hanzi-矩阵、邻接v-值矩阵和泛Topcode-矩阵,并证明了这些Topcode-矩阵之间的一些联系。我们将讨论由上述矩阵得到的代数群、图群、图联网群和数字串群,用于加密动态网络的不同社区。我们矩阵上的操作和结果有助于我们建立整体安全机制来保护网络。

英文摘要:

A mathematical topology with matrix is a natural representation of a coding relational structure that is found in many fields of the world. Matrices are very important in computation of real applications, s ce matrices are easy saved in computer and run quickly, as well as matrices are convenient to deal with communities of current networks, such as Laplacian matrices, adjacent matrices in graph theory. Motivated from convenient, useful and powerful matrices used in computation and investigation of today's networks, we have introduced Topcode-matrices, which are matrices of order $3\times q$ and differ from popular matrices applied in linear algebra and computer science. Topcode-matrices can use numbers, letters, Chinese characters, sets, graphs, algebraic groups \emph{etc.} as their elements. One important thing is that Topcode-matrices of numbers can derive easily number strings, since number strings are text-based passwords used in information security. Topcode-matrices can be used to describe topological graphic passwords (Topsnut-gpws) used in information security and graph connected properties for solving some problems coming in the investigation of Graph Networks and Graph Neural Networks proposed by GoogleBrain and DeepMind. Our topics, in this article, are: Topsnut-matrices, Topcode-matrices, Hanzi-matrices, adjacency ve-value matrices and pan-Topcode-matrices, and some connections between these Topcode-matrices will be proven. We will discuss algebraic groups obtained from the above matrices, graph groups, graph networking groups and number string groups for encrypting different communities of dynamic networks. The operations and results on our matrices help us to set up our overall security mechanism to protect networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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