Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
File(s)
Accepted version
Working paper
Author(s)
Munoz Gonzalez, L
Sgandurra, D
Barrere Cambrun, M
Lupu, EC
Type
Journal Article
Abstract
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.
Date Issued
2019-03-01
Date Acceptance
2016-10-27
Citation
IEEE Transactions on Dependable and Secure Computing, 2019, 16 (2), pp.231-244
ISSN
1941-0018
Publisher
IEEE
Start Page
231
End Page
244
Journal / Book Title
IEEE Transactions on Dependable and Secure Computing
Volume
16
Issue
2
Replaces
10044/1/27017
Copyright Statement
© 2017 The Authors. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/L022729/1
Subjects
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science
Security risk assessment
attack graphs
Bayesian networks
dynamic analysis
probabilistic graphical models
COMPLEXITY
cs.CR
cs.CR
stat.AP
stat.ML
62F15
Strategic, Defence & Security Studies
0803 Computer Software
0804 Data Format
0805 Distributed Computing
Publication Status
Published
Date Publish Online
2017-03-23