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

Authors: Tesleem Fagade; Konstantinos Maraslis; Theo Tryfonas

Addresses: Cryptography Group, University of Bristol, Bristol, UK ' Cryptography Group, University of Bristol, Bristol, UK ' Cryptography Group, University of Bristol, Bristol, UK

Abstract: 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, leading to disparities 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.

Keywords: information security; risk assessment; resource allocation; Monte Carlo simulation; security investment decision.

DOI: 10.1504/IJCIS.2017.088235

International Journal of Critical Infrastructures, 2017 Vol.13 No.2/3, pp.152 - 167

Received: 17 Oct 2016
Accepted: 10 Apr 2017
Published online: 30 Nov 2017 *

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