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Summary:
In this paper, we introduce an innovative optimization framework for selecting security measures, based on a granular, attribute-driven approach inspired by the SABSA methodology. Our model distinguishes between two types of measures: independent measures, which protect against distinct vulnerabilities and whose effects combine multiplicatively, and redundant measures, which mitigate the same vulnerability, with their overall impact determined by the most effective control.The selection problem for security measures is inherently combinatorial and involves nonlinear constraints. To overcome this nonlinearity, we develop tailored linearization techniques, transforming the problem into a mixed-integer linear programming (MILP) formulation.
The results obtained from practical case studies demonstrate that our method effectively identifies optimal or near-optimal solutions while ensuring that the overall residual risk remains below a specified threshold. Finally, the simulation component of our model is fully automated using Python's DOcplex library, which optimally implements an MILP solver.
Author(s):
Pierre Saha Fobougong
Laval University
Canada
Mohamed Mejri
Laval University
Canada
Kamel Adi
Université du Québec à Outaouais
Canada