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Measuring and evaluating network resilience has become an important aspectsince the network is vulnerable to both uncertain disturbances and maliciousattacks. Networked systems are often composed of many dynamic components andchange over time, which makes it difficult for existing methods to access thechangeable situation of network resilience. This paper establishes a novelquantitative framework for evaluating network resilience using the DynamicBayesian Network. The proposed framework can be used to evaluate the network'smulti-stage resilience processes when suffering various attacks and recoveries.First, we define the dynamic capacities of network components and establish thenetwork's five core resilience capabilities to describe the resilientnetworking stages including preparation, resistance, adaptation, recovery, andevolution; the five core resilience capabilities consist of rapid responsecapability, sustained resistance capability, continuous running capability,rapid convergence capability, and dynamic evolution capability. Then, we employa two-time slices approach based on the Dynamic Bayesian Network to quantifyfive crucial performances of network resilience based on core capabilitiesproposed above. The proposed approach can ensure the time continuity ofresilience evaluation in time-varying networks. Finally, our proposedevaluation framework is applied to different attacks and recovery conditions intypical simulations and real-world network topology. Results and comparisonswith extant studies indicate that the proposed method can achieve a moreaccurate and comprehensive evaluation and can be applied to network scenariosunder various attack and recovery intensities.

A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network