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Measuring the Network Vulnerability Based on Markov Criticality

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Published:21 July 2021Publication History
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Abstract

Vulnerability assessment—a critical issue for networks—attempts to foresee unexpected destructive events or hostile attacks in the whole system. In this article, we consider a new Markov global connectivity metric—Kemeny constant, and take its derivative called Markov criticality to identify critical links. Markov criticality allows us to find links that are most influential on the derivative of Kemeny constant. Thus, we can utilize it to identity a critical link (i, j) from node i to node j, such that removing it leads to a minimization of networks’ global connectivity, i.e., the Kemeny constant. Furthermore, we also define a novel vulnerability index to measure the average speed by which we can disconnect a specified ratio of links with network decomposition. Our method is of high efficiency, which can be easily employed to calculate the Markov criticality in real-life networks. Comprehensive experiments on several synthetic and real-life networks have demonstrated our method’s better performance by comparing it with state-of-the-art baseline approaches.

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    • Published in

      cover image ACM Transactions on Knowledge Discovery from Data
      ACM Transactions on Knowledge Discovery from Data  Volume 16, Issue 2
      April 2022
      514 pages
      ISSN:1556-4681
      EISSN:1556-472X
      DOI:10.1145/3476120
      Issue’s Table of Contents

      Copyright © 2021 Association for Computing Machinery.

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      Publication History

      • Published: 21 July 2021
      • Accepted: 1 May 2021
      • Revised: 1 April 2021
      • Received: 1 January 2021
      Published in tkdd Volume 16, Issue 2

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