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.