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.

A self-organized method for computing the epidemic threshold in computer networks / Bagnoli, Franco; Bellini, Emanuele; Massaro, Emanuele. - STAMPA. - 11193:(2018), pp. 119-130. (Intervento presentato al convegno Internet Science tenutosi a St. Petersburg, Russia nel October 24–26, 2018) [10.1007/978-3-030-01437-7_10].

A self-organized method for computing the epidemic threshold in computer networks

Bagnoli, Franco
;
Bellini, Emanuele;Massaro, Emanuele
2018

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.
2018
Internet Science
Internet Science
St. Petersburg, Russia
October 24–26, 2018
Bagnoli, Franco; Bellini, Emanuele; Massaro, Emanuele
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1140475
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