Initial growth rates of malware epidemics fail to predict their reach

Abstract Empirical studies show that epidemiological models based on an epidemic’s initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pande...

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Autores principales:	Lev Muchnik, Elad Yom-Tov, Nir Levy, Amir Rubin, Yoram Louzoun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9841653237bd48d49ef3cb426b8218f8
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spelling	oai:doaj.org-article:9841653237bd48d49ef3cb426b8218f82021-12-02T17:50:41ZInitial growth rates of malware epidemics fail to predict their reach10.1038/s41598-021-91321-02045-2322https://doaj.org/article/9841653237bd48d49ef3cb426b8218f82021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91321-0https://doaj.org/toc/2045-2322Abstract Empirical studies show that epidemiological models based on an epidemic’s initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population’s susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic’s initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.Lev MuchnikElad Yom-TovNir LevyAmir RubinYoram LouzounNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lev Muchnik Elad Yom-Tov Nir Levy Amir Rubin Yoram Louzoun Initial growth rates of malware epidemics fail to predict their reach
description	Abstract Empirical studies show that epidemiological models based on an epidemic’s initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population’s susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic’s initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.
format	article
author	Lev Muchnik Elad Yom-Tov Nir Levy Amir Rubin Yoram Louzoun
author_facet	Lev Muchnik Elad Yom-Tov Nir Levy Amir Rubin Yoram Louzoun
author_sort	Lev Muchnik
title	Initial growth rates of malware epidemics fail to predict their reach
title_short	Initial growth rates of malware epidemics fail to predict their reach
title_full	Initial growth rates of malware epidemics fail to predict their reach
title_fullStr	Initial growth rates of malware epidemics fail to predict their reach
title_full_unstemmed	Initial growth rates of malware epidemics fail to predict their reach
title_sort	initial growth rates of malware epidemics fail to predict their reach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9841653237bd48d49ef3cb426b8218f8
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Initial growth rates of malware epidemics fail to predict their reach

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