A Self-Organized Method for Computing the Epidemic Threshold in Computer Networks

TL;DR

This paper introduces a self-organized, simulation-free method to determine the epidemic threshold in computer networks, accounting for local infection risk perception and complex interference effects.

Contribution

It presents a novel approach to compute epidemic thresholds efficiently without repeated simulations, adaptable to local risk perception and complex network dynamics.

Findings

Method accurately estimates epidemic thresholds without simulations

Applicable to networks with local risk perception

Handles complex interference scenarios

Abstract

In many cases, tainted information in a computer network can spread in a way similar to an epidemics in the human world. On the other had, information processing paths are often redundant, so a single infection occurrence can be easily "reabsorbed". Randomly checking the information with a central server is equivalent to lowering the infection probability but with a certain cost (for instance processing time), so it is important to quickly evaluate the epidemic threshold for each node. We present a method for getting such information without resorting to repeated simulations. As for human epidemics, the local information about the infection level (risk perception) can be an important factor, and we show that our method can be applied to this case, too. Finally, when the process to be monitored is more complex and includes "disruptive interference", one has to use actual simulations, which however can be carried out "in parallel" for many possible infection probabilities.