Optimisation of data collection strategies for model-based evaluation and decision-making: poster

Abstract

Probabilistic and stochastic models are routinely used in performance, dependability and, more recently, security evaluation. Determining appropriate values for model parameters is a long-standing problem in the practical use of such models. With the increasing emphasis on human aspects and business considerations, data collection to estimate parameter values often gets prohibitively expensive, since it may involve questionnaires, costly audits or additional monitoring and processing. This work aims to facilitate the design of optimal data collection strategies for such models, looking especially at application in security decision-making. We discuss related literature and illustrate the main idea behind out approach for optimising data collection for model-based system evaluation.