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Towards effective cybersecurity resource allocation: the Monte Carlo predictive modelling approach

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)152-167
Number of pages16
JournalInternational Journal of Critical Infrastructures
Issue number2-3
Early online date1 Dec 2017
DateAccepted/In press - 6 Nov 2017
DateE-pub ahead of print - 1 Dec 2017
DatePublished (current) - Dec 2017


Organisations invest in technical and procedural capabilities to ensure the confidentiality, integrity and availability of information assets and sustain business continuity at all times. However, given growing productive assets and limited protective security budgets, there is a need for deliberate evaluation of information security investment. Optimal resource allocation to security is often affected by intrinsically uncertain variables and associated factors like technical, economical and psychological; therefore, security expenditure is a crucial resource allocation decision. In spite of that, security managers and business owners are often incentivised by different drivers on whether to allocate optimal resources to cyber-specific security protective assets or other business productive assets. Hence, there is a disparity of opinion in resource allocation decisions. We explored how Monte Carlo predictive simulation model can be used within the context of Information Technology to reduce these disparities. Using a conceptual enterprise as a case study and verifiable historical cost of security breaches as parametric values, our model shows why using conventional risk assessment approach as budgeting process can result in significant over/under allocation of resources for cyber capabilities. Our model can serve as a benchmark for policy and decision support to aid stakeholders in optimising resource allocation for cyber security investments.

    Research areas

  • Information Security, risk assessment, Resource allocation, Monte-Carlo simulation, Security investment decision

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Inderscience at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 382 KB, PDF document


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