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Beschreibung:
A mathematical framework is presented that describes risk in the context of safety and security problems quantitatively and in an integrative way. Great importance is laid on a clear notation with a sound semantics. Essentially, this seminal contribution is a substantially expanded version of our short paper “A quantitative risk model for a uniform description of safety and security”, which we presented to the 10th Future Security 2015 in Berlin (A quantitative risk model for a uniform description of safety and security. In: Proceedings of the 10th Future security—security research conference, pp 317–324, 2015). The key concept of this paper is a quantitative formulation of risk. Uncertainties are modelled based on probability distributions. Risk due to purely stochastic sources of danger is based on objective notions of probabilities and costs whereas risks of individuals (intelligent agents) are described from their own points of view, i.e. in a fully subjective manner, since individuals draw their decisions based on their subjective assessments of potential costs and of frequencies of event occurrence. Therefore, probability is interpreted in a Bayesian context as a degree of belief (DoB). Based on a role model for the involved agents with the three roles »source of danger«, »subject of protection« and »protector«, risk is modelled quantitatively using statistical decision theory and game theory. The set D of sources of danger is endowed with a DoB-distribution describing the probability of occurrence. D is partitioned into subsets that describe dangers which are due to random causes, carelessness and intention. A set of flanks of vulnerability F is assigned to each subject of protection. These flanks characterize different aspects of vulnerability concerning mechanical, physiological, informational, economical, reputational, psychological, … vulnerability. The flanks of vulnerability are endowed with conditional DoBs that describe to which degree an incidence or an attack will be harmful. Additionally, each ...