Fast Response to Infection Spread and Cyber Attacks on Large-Scale Networks
This work addresses the challenge of efficient response in complex networks for applications like cybersecurity and epidemiology, but it appears incremental as it builds on existing multiscale methods without introducing a new paradigm.
The authors tackled the problem of quickly responding to infection spread and cyber attacks on large-scale weighted networks by developing a multiscale strategy to approximate the system at multiple scales and combine coarse-scale information to solve a microscopic-scale optimization problem based on the SIS epidemiological model, aiming to detect nodes for immunization to maintain low infection levels.
We present a strategy for designing fast methods of response to cyber attacks and infection spread on complex weighted networks. In these networks, nodes can be interpreted as primitive elements of the system, and weighted edges reflect the strength of interaction among these elements. The proposed strategy belongs to the family of multiscale methods whose goal is to approximate the system at multiple scales of coarseness and to obtain a solution of microscopic scale by combining the information from coarse scales. In recent years these methods have demonstrated their potential for solving optimization and analysis problems on large-scale networks. We consider an optimization problem that is based on the SIS epidemiological model. The objective is to detect the network nodes that have to be immunized in order to keep a low level of infection in the system.