Evolving OWA operators for cyber security decision making problems

Designing secure software systems is a non-trivial task as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, a great deal of expertise is required to assess the security risks posed to a proposed system in its design stage. In this research we demonstrate how Evolutionary Algorithms (EAs) and Simulated Annealing (SA) can be used with Ordered Weighted Average (OWA) operators to provide a suitable aggregation tool for combining experts' opinions of individual components of an specific technical attack to produce an overall rating that can be used to rank attacks in order of salience. A set of thirty nine cyber security experts took part in an exercise in which they independently assessed a realistic system scenario. We show that using EAs and SA, OWA operators can be tuned to produce aggregations that are more stable when applied to a group of experts' ratings than those produced by the arithmetic mean, and that the difference between the solutions found by each of the algorithms is minimal. However, EAs do prove to be a quicker method of search when an equivalent number of evaluations is performed by each method.