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Modeling multivariate cybersecurity risks

Author

Listed:
  • Chen Peng
  • Maochao Xu
  • Shouhuai Xu
  • Taizhong Hu

Abstract

Modeling cybersecurity risks is an important, yet challenging, problem. In this paper, we initiate the study of modeling multivariate cybersecurity risks. We develop the first statistical approach, which is centered at a Copula-GARCH model that uses vine copulas to model the multivariate dependence exhibited by real-world cyber attack data. We find that ignoring the due multivariate dependence causes a severe underestimation of cybersecurity risks. Both simulation and empirical studies show that the proposed approach leads to accurate predictions of multivariate cybersecurity risks.

Suggested Citation

  • Chen Peng & Maochao Xu & Shouhuai Xu & Taizhong Hu, 2018. "Modeling multivariate cybersecurity risks," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(15), pages 2718-2740, November.
  • Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2718-2740
    DOI: 10.1080/02664763.2018.1436701
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    Cited by:

    1. Da, Gaofeng & Xu, Maochao & Zhao, Peng, 2021. "Multivariate dependence among cyber risks based on L-hop propagation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 525-546.
    2. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    3. Blakely, Benjamin & Kurtenbach, Jim & Nowak, Lovila, 2022. "Exploring the information content of cyber breach reports and the relationship to internal controls," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    4. Ding, Weiyong & Xu, Maochao & Huang, Yu & Zhao, Peng & Song, Fengyi, 2021. "Cyber attacks on PMU placement in a smart grid: Characterization and optimization," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    5. Daniel Zängerle & Dirk Schiereck, 2023. "Modelling and predicting enterprise-level cyber risks in the context of sparse data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 434-462, April.
    6. Gabriela Zeller & Matthias Scherer, 2023. "Risk mitigation services in cyber insurance: optimal contract design and price structure," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 502-547, April.
    7. Dacorogna, Michel & Debbabi, Nehla & Kratz, Marie, 2023. "Building up cyber resilience by better grasping cyber risk via a new algorithm for modelling heavy-tailed data," European Journal of Operational Research, Elsevier, vol. 311(2), pages 708-729.
    8. Ma, Boyuan & Chu, Tingjin & Jin, Zhuo, 2022. "Frequency and severity estimation of cyber attacks using spatial clustering analysis," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 33-45.
    9. Zängerle, Daniel & Schiereck, Dirk, 2022. "Modelling and predicting enterprise‑level cyber risks in the context of sparse data availability," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136276, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).

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