Dynamic Security Risk Management Using Bayesian Attack Graphs

TLDR

Security risk assessment and mitigation are essential for maintaining productive IT infrastructures, yet existing models such as attack graphs and trees fail to capture causal dependencies between network states and ignore resource constraints in optimization. The paper proposes a Bayesian network–based risk management framework that quantifies network compromise probabilities at multiple levels and guides administrators in developing mitigation and management plans. The framework employs Bayesian networks coupled with a multi‑objective optimization platform to supply administrators with trade‑off information for resource‑constrained decision making. Unlike other models, the proposed risk model supports dynamic analysis during network deployment.

Abstract

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.