Volltext-Downloads (blau) und Frontdoor-Views (grau)

Building resilience in cybersecurity: An artificial lab approach

  • Based on classical contagion models we introduce an artificial cyber lab: the digital twin of a complex cyber system in which possible cyber resilience measures may be implemented and tested. Using the lab, in numerical case studies, we identify two classes of measures to control systemic cyber risks: security‐ and topology‐based interventions. We discuss the implications of our findings on selected real‐world cybersecurity measures currently applied in the insurance and regulation practice or under discussion for future cyberrisk control. To this end, we provide a brief overview of the current cybersecurity regulation and emphasize the role of insurance companies as private regulators. Moreover, from an insurance point of view, we provide first attempts to design systemic cyber risk obligations and to measure the systemic risk contribution of individual policyholders.

Download full text files

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Kerstin AwiszusORCiD, Yannick Bell, Jan Lüttringhaus, Gregor Svindland, Alexander Voß, Stefan Weber
URN:urn:nbn:de:bsz:960-opus4-30090
DOI:https://doi.org/10.25968/opus-3009
DOI original:https://doi.org/10.1111/jori.12450
ISSN:1539-6975
Parent Title (English):Journal of Risk and Insurance
Document Type:Article
Language:English
Year of Completion:2024
Publishing Institution:Hochschule Hannover
Release Date:2024/12/19
Tag:complex systems; complexity economics; cyber insurance; cyberresilience; cybersecurity; economics of networks; systemic cyberrisks
GND Keyword:Komplexes System; Cyber-Versicherung; Computersicherheit; Resilienz
Volume:91
Issue:3
First Page:753
Last Page:800
Link to catalogue:1914513940
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:330 Wirtschaft
004 Informatik
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International