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      Text-based Passwords Generated From Topological Graphic Passwords

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          Abstract

          Topological graphic passwords (Topsnut-gpws) are one of graph-type passwords, but differ from the existing graphical passwords, since Topsnut-gpws are saved in computer by algebraic matrices. We focus on the transformation between text-based passwords (TB-paws) and Topsnut-gpws in this article. Several methods for generating TB-paws from Topsnut-gpws are introduced; these methods are based on topological structures and graph coloring/labellings, such that authentications must have two steps: one is topological structure authentication, and another is text-based authentication. Four basic topological structure authentications are introduced and many text-based authentications follow Topsnut-gpws. Our methods are based on algebraic, number theory and graph theory, many of them can be transformed into polynomial algorithms. A new type of matrices for describing Topsnut-gpws is created here, and such matrices can produce TB-paws in complex forms and longer bytes. Estimating the space of TB-paws made by Topsnut-gpws is very important for application. We propose to encrypt dynamic networks and try to face: (1) thousands of nodes and links of dynamic networks; (2) large numbers of Topsnut-gpws generated by machines rather than human's hands. As a try, we apply spanning trees of dynamic networks and graphic groups (Topsnut-groups) to approximate the solutions of these two problems. We present some unknown problems in the end of the article for further research.

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          Connected Dominating Set in Sensor Networks and MANETs

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            An exact algorithm for the Maximum Leaf Spanning Tree problem

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              Maximal planar scale-free Sierpinski networks with small-world effect and power-law strength-degree correlation

              Many real networks share three generic properties: they are scale-free, display a small-world effect, and show a power-law strength-degree correlation. In this paper, we propose a type of deterministically growing networks called Sierpinski networks, which are induced by the famous Sierpinski fractals and constructed in a simple iterative way. We derive analytical expressions for degree distribution, strength distribution, clustering coefficient, and strength-degree correlation, which agree well with the characterizations of various real-life networks. Moreover, we show that the introduced Sierpinski networks are maximal planar graphs.
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                Author and article information

                Journal
                12 September 2018
                Article
                1809.04727
                0fe0555f-8b0d-45eb-a299-5d39667a1ef7

                http://creativecommons.org/licenses/by/4.0/

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                Custom metadata
                cs.IT math.CO math.IT

                Numerical methods,Combinatorics,Information systems & theory
                Numerical methods, Combinatorics, Information systems & theory

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