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arXiv · 2108.09040

A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network

Shanqing Jiang·Lin Yang·Guang Cheng·Xianming Gao·Tao Feng·Yuyang Zhou
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Abstract

Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. Networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian Network. The proposed framework can be used to evaluate the network's multi-stage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution; the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian Network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve a more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensities.

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Shanqing Jiang, Lin Yang, Guang Cheng, Xianming Gao, Tao Feng, Yuyang Zhou. 2021-08-20. A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network. https://arxiv.org/abs/2108.09040

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