# Efficient and Generic Algorithms for Quantitative Attack Tree Analysis

arXiv: https://arxiv.org/abs/2212.05358 · HTML: https://mlsanity.com/paper/2212.05358/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [Fuzzy Fault Trees Formalized](https://mlsanity.com/paper/2403.08843/) (2024)
- [WATCHDOG: an ontology-aWare risk AssessmenT approaCH via object-oriented DisruptiOn Graphs](https://mlsanity.com/paper/2412.13964/) (2024)
- [Actual causality in fault trees](https://mlsanity.com/paper/2607.01840/) (2026)

## Abstract (arXiv)

Numerous analysis methods for quantitative attack tree analysis have been proposed. These algorithms compute relevant security metrics, i.e. performance indicators that quantify how good the security of a system is; typical metrics being the most likely attack, the cheapest, or the most damaging one. However, existing methods are only geared towards specific metrics or do not work on general attack trees. This paper classifies attack trees in two dimensions: proper trees vs. directed acyclic graphs (i.e. with shared subtrees); and static vs. dynamic gates. For three out of these four classes, we propose novel algorithms that work over a generic attribute domain, encompassing a large number of concrete security metrics defined on the attack tree semantics; dynamic attack trees with directed acyclic graph structure are left as an open problem. We also analyse the computational complexity of our methods.