Efficient Attack Graph Analysis through Approximate Inference
- Submitting institution
-
Royal Holloway and Bedford New College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30110729
- Type
- D - Journal article
- DOI
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10.1145/3105760
- Title of journal
- ACM Transactions on Information and System Security
- Article number
- 10
- First page
- 1
- Volume
- 20
- Issue
- 3
- ISSN
- 1094-9224
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work is the first to propose the use of approximate inference techniques for the scalable analysis of attack graphs. Furthermore, it proposes and compare both the sequential and parallel implementations of approximate inference technique to estimate the probabilities of compromise for the network nodes. The accuracy of approximate inference techniques is sufficient for many practical needs exhibiting a root-mean-squared error smaller than 0.03. Finally, this work shows that it is possible to get accurate results before the algorithm fully converges by monitoring the probability estimates at each iteration, enabling planning of risk mitigation strategies in advance.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -