25,381 research outputs found
Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
Attack graphs are a powerful tool for security risk assessment by analysing
network vulnerabilities and the paths attackers can use to compromise network
resources. The uncertainty about the attacker's behaviour makes Bayesian
networks suitable to model attack graphs to perform static and dynamic
analysis. Previous approaches have focused on the formalization of attack
graphs into a Bayesian model rather than proposing mechanisms for their
analysis. In this paper we propose to use efficient algorithms to make exact
inference in Bayesian attack graphs, enabling the static and dynamic network
risk assessments. To support the validity of our approach we have performed an
extensive experimental evaluation on synthetic Bayesian attack graphs with
different topologies, showing the computational advantages in terms of time and
memory use of the proposed techniques when compared to existing approaches.Comment: 14 pages, 15 figure
A probabilistic framework for tracking in wide-area environments
Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN). In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.<br /
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