The complexity of estimating systematic risk in networks

Benjamin Johnson, Aron Laszka, Jens Grossklags

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


This risk of catastrophe from an attack is a consequence of a network's structure formed by the connected individuals, businesses and computer systems. Understanding the likelihood of extreme events, or, more generally, the probability distribution of the number of compromised nodes is an essential requirement to provide risk-mitigation or cyber-insurance. However, previous network security research has not considered features of these distributions beyond their first central moments, while previous cyber-insurance research has not considered the effect of topologies on the supply side. We provide a mathematical basis for bridging this gap: we study the complexity of computing these loss-number distributions, both generally and for special cases of common real-world networks. In the case of scale-free networks, we demonstrate that expected loss alone cannot determine the riskiness of a network, and that this riskiness cannot be naively estimated from smaller samples, which highlights the lack/importance of topological data in security incident reporting.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE 27th Computer Security Foundations Symposium, CSF 2014
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781479942909
StatePublished - Nov 13 2014
Event27th IEEE Computer Security Foundations Symposium, CSF 2014 - Vienna, Austria
Duration: Jul 19 2014Jul 22 2014

Publication series

NameProceedings of the Computer Security Foundations Workshop
ISSN (Print)1063-6900


Other27th IEEE Computer Security Foundations Symposium, CSF 2014

All Science Journal Classification (ASJC) codes

  • Software


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