# Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

arXiv: https://arxiv.org/abs/1510.02427 · HTML: https://mlsanity.com/paper/1510.02427/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2015.

## Built on by (most cited first, 2 of 2)

- [Dynamic Risk Assessment by Bayesian Attack Graphs and Process Mining](https://mlsanity.com/paper/2604.18080/) (2026)
- [Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing](https://mlsanity.com/paper/2609.07344/) (2026)

## Abstract (arXiv)

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.