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Dynamic Security Risk Management Using Bayesian Attack Graphs

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3 Author(s)
Poolsappasit, N. ; Dept. of Comput. Sci., Missouri Univ. of Sci. & Technol., Rolla, MO, USA ; Dewri, R. ; Ray, I.

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.

Published in:

Dependable and Secure Computing, IEEE Transactions on  (Volume:9 ,  Issue: 1 )

Date of Publication:

Jan.-Feb. 2012

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