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Pubblication Details
Authors:Daniele Codetta Raiteri
Luigi Portinale
Roberta Terruggia
Scientific Area:Artificial Intelligence
Probabilistic Graphical Models
Computer Security
Title:Quantitative Evaluation of Attack/Defense Scenarios through Decision Network Modelling and Analysis
Published on:Proc. IEEE Intern. Carnhan Conference on Security Technology (ICCST 2014)
Pages: 432-437
Publisher:IEEE
Year:2014
Tipo Pubblicazione:Paper on Proceedings International Conference
Abstract:We exploit Decision Networks (DN) for the analysis of attack/defense scenarios. DN extend both the modeling and the analysis capabilities of formalisms based on Attack Trees, which are the main reference model in such a context. In particular, DN can naturally address uncertainty at every level, including the interaction level of attacks and countermeasures, making possible the modeling of situations which are not limited to Boolean combinations of events. Furthermore, inference algorithms can be exploited for a probabilistic analysis with the goal of assessing the risk and the importance of the attacks (with respect to specific sets of countermeasures), and selecting the optimal set (with respect to a specific objective function) of countermeasures to activate.