The growing need to find proper countermeasures able to protect critical infrastructures from threats has addressed the definition of quantitative methodologies for risk assessment. One of the most difficult aspects in this topic is the evaluation of the effects of attacks. Attacks Trees represent one of the most used formalisms in the modeling of attack scenarios: notwithstanding some extensions have been proposed to enrich the expressiveness of the original formalism, some effort should be spent on their analyzability. This paper defines a transformational approach that translates Attack Trees into Bayesian Networks. The proposed approach can cope with different Attack Trees extensions; moreover, it allows the quantitative evaluation of combined attacks modelled as a set of Attack Trees.

Exploiting Bayesian Networks for the analysis of combined Attack Trees

GRIBAUDO, MARCO;
2015-01-01

Abstract

The growing need to find proper countermeasures able to protect critical infrastructures from threats has addressed the definition of quantitative methodologies for risk assessment. One of the most difficult aspects in this topic is the evaluation of the effects of attacks. Attacks Trees represent one of the most used formalisms in the modeling of attack scenarios: notwithstanding some extensions have been proposed to enrich the expressiveness of the original formalism, some effort should be spent on their analyzability. This paper defines a transformational approach that translates Attack Trees into Bayesian Networks. The proposed approach can cope with different Attack Trees extensions; moreover, it allows the quantitative evaluation of combined attacks modelled as a set of Attack Trees.
2015
Attack Trees; Bayesian Networks; Model transformations; Quantitative risk assessement; Theoretical Computer Science; Computer Science (all)
File in questo prodotto:
File Dimensione Formato  
11311-971173 Gribaudo.pdf

accesso aperto

: Publisher’s version
Dimensione 905.8 kB
Formato Adobe PDF
905.8 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/971173
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 27
  • ???jsp.display-item.citation.isi??? 20
social impact