基于动态贝叶斯网络的网络弹性定量评估框架
A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network
摘要：
由于网络易受不确定干扰和恶意攻击的影响，测量和评估网络弹性已成为一个重要方面。网络系统通常由许多动态组件组成，并且随时间变化，这使得现有的方法很难获得网络弹性的变化情况。提出了一种基于动态贝叶斯网络的网络弹性定量评估方法。该框架可用于评估网络在遭受各种攻击和恢复时的多阶段弹性过程。首先，我们定义了网络组件的动态能力，并建立了网络的五个核心弹性能力来描述弹性网络的阶段，包括准备，抵抗，适应，恢复和进化;五个核心弹性能力包括快速响应能力，持续抵抗能力，持续运行能力，快速收敛能力和动态进化能力。然后，我们采用一个基于动态贝叶斯网络的两个时间片的方法来量化五个关键性能的网络弹性的基础上提出的核心能力。该方法能够保证时变网络弹性评估的时间连续性。最后，我们提出的评估框架适用于不同的攻击和恢复条件下，在典型的模拟和现实世界的网络拓扑结构。结果表明，该方法能够对攻击强度和恢复强度进行更准确、更全面的评估，适用于各种攻击和恢复强度下的网络场景。
英文摘要：
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.
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。
