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A Robust Approach for Mitigating Risks in Cyber Supply Chains

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  • Kaiyue Zheng
  • Laura A. Albert

Abstract

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the (1−α) worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.

Suggested Citation

  • Kaiyue Zheng & Laura A. Albert, 2019. "A Robust Approach for Mitigating Risks in Cyber Supply Chains," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 2076-2092, September.
  • Handle: RePEc:wly:riskan:v:39:y:2019:i:9:p:2076-2092
    DOI: 10.1111/risa.13269
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    References listed on IDEAS

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    1. Mark S. Daskin, 1983. "A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution," Transportation Science, INFORMS, vol. 17(1), pages 48-70, February.
    2. Scaparra, Maria P. & Church, Richard L., 2008. "An exact solution approach for the interdiction median problem with fortification," European Journal of Operational Research, Elsevier, vol. 189(1), pages 76-92, August.
    3. Laura McLay & Casey Rothschild & Seth Guikema, 2012. "Robust Adversarial Risk Analysis: A Level- k Approach," Decision Analysis, INFORMS, vol. 9(1), pages 41-54, March.
    4. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
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    Cited by:

    1. Michael Greenberg & Anthony Cox & Vicki Bier & Jim Lambert & Karen Lowrie & Warner North & Michael Siegrist & Felicia Wu, 2020. "Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2113-2127, November.
    2. Kaiyue Zheng & Laura A. Albert, 2019. "Interdiction models for delaying adversarial attacks against critical information technology infrastructure," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(5), pages 411-429, August.
    3. Cheung, Kam-Fung & Bell, Michael G.H. & Bhattacharjya, Jyotirmoyee, 2021. "Cybersecurity in logistics and supply chain management: An overview and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    4. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

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