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

Fundamental concepts in management research and ensuring research quality : focusing on case study method

This paper discusses fundamental concepts in management research and ensuring research quality. It was presented at the European Academy of Management annual conference in 2008

Decomposition of structural learning about directed acyclic graphs

AbstractIn this paper, we propose that structural learning of a directed acyclic graph can be decomposed into problems related to its decomposed subgraphs. The decomposition of structural learning requires conditional independencies, but it does not require that separators are complete undirected subgraphs. Domain or prior knowledge of conditional independencies can be utilized to facilitate the decomposition of structural learning. By decomposition, search for d-separators in a large network is localized to small subnetworks. Thus both the efficiency of structural learning and the power of conditional independence tests can be improved