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Optimal selection of IT security safeguards from an existing knowledge base

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  • Schilling, Andreas
  • Werners, Brigitte

Abstract

In this paper, a combinatorial optimization model is proposed to efficiently select security safeguards in order to protect IT infrastructures and systems. The approach is designed to provide very concrete decision support for an organization as a whole or separately for specific systems. It can be applied in practice without requiring the decision maker himself to collect extensive input data. This is accomplished by using an existing comprehensive and highly accepted knowledge base as a basis for decision making. For our analysis, we use the publicly available IT baseline protection catalogues of the German Federal Office for Information Security (BSI). The catalogues contain more than 500 threats and over 1200 safeguard alternatives to choose from. Applying our model, it is possible to make use of this knowledge and determine optimal selections of safeguards according to given security requirements. The approach supports the decision maker in establishing an effective baseline security strategy.

Suggested Citation

  • Schilling, Andreas & Werners, Brigitte, 2016. "Optimal selection of IT security safeguards from an existing knowledge base," European Journal of Operational Research, Elsevier, vol. 248(1), pages 318-327.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:1:p:318-327
    DOI: 10.1016/j.ejor.2015.06.048
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    References listed on IDEAS

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    Cited by:

    1. Barbati, M. & Figueira, J.R. & Greco, S. & Ishizaka, A. & Panaro, S., 2023. "A multiple criteria methodology for priority based portfolio selection," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Michel Benaroch, 2018. "Real Options Models for Proactive Uncertainty-Reducing Mitigations and Applications in Cybersecurity Investment Decision Making," Information Systems Research, INFORMS, vol. 29(2), pages 315-340, June.
    3. Khouzani, MHR. & Liu, Zhengliang & Malacaria, Pasquale, 2019. "Scalable min-max multi-objective cyber-security optimisation over probabilistic attack graphs," European Journal of Operational Research, Elsevier, vol. 278(3), pages 894-903.
    4. David Rios Insua & Aitor Couce‐Vieira & Jose A. Rubio & Wolter Pieters & Katsiaryna Labunets & Daniel G. Rasines, 2021. "An Adversarial Risk Analysis Framework for Cybersecurity," Risk Analysis, John Wiley & Sons, vol. 41(1), pages 16-36, January.
    5. Lee, Sangjae & Costello, Francis Joseph & Lee, Kun Chang, 2021. "Hierarchical balanced scorecard-based organizational goals and the efficiency of controls processes," Journal of Business Research, Elsevier, vol. 132(C), pages 270-288.
    6. Paul, Jomon A. & Zhang, Minjiao, 2021. "Decision support model for cybersecurity risk planning: A two-stage stochastic programming framework featuring firms, government, and attacker," European Journal of Operational Research, Elsevier, vol. 291(1), pages 349-364.
    7. Wang, Zhen & Li, Chaofan & Jin, Xing & Ding, Hong & Cui, Guanghai & Yu, Lanping, 2021. "Evolutionary dynamics of the interdependent security games on complex network," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    8. Liesiö, Juuso & Salo, Ahti & Keisler, Jeffrey M. & Morton, Alec, 2021. "Portfolio decision analysis: Recent developments and future prospects," European Journal of Operational Research, Elsevier, vol. 293(3), pages 811-825.

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