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Issue No. 01 - January/February (2012 vol. 9)
ISSN: 1545-5971
pp: 61-74
Nayot Poolsappasit , Missouri University of Science and Technology, Rolla
Rinku Dewri , University of Denver, Denver
Indrajit Ray , Colorado State University, Fort Collins
Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model. In this paper, we propose a risk management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels. We show how to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multiobjective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.
Security risk assessment, mitigation analysis, Bayesian belief networks, attack graph.

N. Poolsappasit, R. Dewri and I. Ray, "Dynamic Security Risk Management Using Bayesian Attack Graphs," in IEEE Transactions on Dependable and Secure Computing, vol. 9, no. , pp. 61-74, 2011.
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