Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



e.c.lupu Website




564Huxley BuildingSouth Kensington Campus






BibTex format

author = {Munoz, Gonzalez L and Sgandurra, D and Barrere, Cambrun M and Lupu, EC},
doi = {10.1109/TDSC.2016.2627033},
journal = {IEEE Transactions on Dependable and Secure Computing},
pages = {231--244},
title = {Exact Inference Techniques for the Analysis of Bayesian Attack Graphs},
url = {},
volume = {16},
year = {2019}

RIS format (EndNote, RefMan)

AB - 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.
AU - Munoz,Gonzalez L
AU - Sgandurra,D
AU - Barrere,Cambrun M
AU - Lupu,EC
DO - 10.1109/TDSC.2016.2627033
EP - 244
PY - 2019///
SN - 1941-0018
SP - 231
TI - Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
T2 - IEEE Transactions on Dependable and Secure Computing
UR -
UR -
VL - 16
ER -