生物启发的复杂网络中保护资源分配框架
Bio-Inspired Framework for Allocation of Protection Resources in Cyber-Physical Networks
AI总结：
本文提出生物启发的保护资源分配框架，通过预防和纠正资源控制传播过程，结合动态系统理论和凸优化，实现成本最优的资源分配，同时考虑更广泛的流行病模型和网络不确定性。
摘要：
在本章中，我们考虑设计保护策略以遏制复杂网络中传播过程的扩散问题。我们利用生物启发的传播模型，如Susceptible-Infected-Susceptible（SIS）模型，来阐述我们的想法。我们首先引入一个框架，允许分配两种类型的资源以遏制传播：（i）预防资源可降低传播速率，（ii）纠正资源可提高分配节点的恢复速率。在实践中，这些资源有相关成本，取决于预防资源的韧性水平或纠正资源的恢复效率。我们提出一个基于动态系统理论和凸优化的数学框架，以找到网络中成本最优的保护资源分配方案。我们还提出两个框架扩展：（i）考虑更广泛的流行病模型，超越简单的SIS模型；（ii）假设传播过程中接触网络的不确定性。我们比较这些保护策略与文献中常见的启发式方法，并通过航空交通网络的数值模拟来展示结果。
英文摘要：
In this chapter, we consider the problem of designing protection strategies to contain spreading processes in complex cyber-physical networks. We illustrate our ideas using a family of bio-motivated spreading models originally proposed in the epidemiological literature, e.g., the Susceptible-Infected-Susceptible (SIS) model. We first introduce a framework in which we are allowed to distribute two types of resources in order to contain the spread, namely, (i) preventive resources able to reduce the spreading rate, and (ii) corrective resources able to increase the recovery rate of nodes in which the resources are allocated. In practice, these resources have an associated cost that depends on either the resiliency level achieved by the preventive resource, or the restoration efficiency of the corrective resource. We present a mathematical framework, based on dynamic systems theory and convex optimization, to find the cost-optimal distribution of protection resources in a network to contain the spread. We also present two extensions to this framework in which (i) we consider generalized epidemic models, beyond the simple SIS model, and (ii) we assume uncertainties in the contact network in which the spreading is taking place. We compare these protection strategies with common heuristics previously proposed in the literature and illustrate our results with numerical simulations using the air traffic network.
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