Bio-Inspired Framework for Allocation of Protection Resources in Cyber-Physical Networks

TL;DR

This paper proposes a bio-inspired, mathematically grounded framework for optimally allocating protective resources in cyber-physical networks to contain spreading processes, considering costs, uncertainties, and generalized models.

Contribution

It introduces a convex optimization-based method for cost-efficient protection resource distribution, extending to generalized epidemic models and uncertain contact networks.

Findings

Optimal resource allocation reduces spread effectively.

Framework outperforms heuristic strategies in simulations.

Applicable to complex networks like air traffic systems.

Abstract

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.