Cluster-based vulnerability assessment of operating systems and web browsers
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1243
- Type
- D - Journal article
- DOI
-
10.1007/s00607-018-0663-0
- Title of journal
- Computing
- Article number
- -
- First page
- 139
- Volume
- 101
- Issue
- 2
- ISSN
- 0010-485X
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- 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
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Extension of a paper previously published at IEEE EDCC 2017 in the conference's "Distinguished papers" session (1 of 3 papers, out of 65 submissions). Output stems from collaboration with University of Maryland, and is the first in a series of three journal papers between 2018-2020 that utilise vulnerability report times in public security databases for security assessment and forecasting. This output shows the proposed approach outperforms vulnerability discovery models without clustering in almost all the cases we applied to operating systems’ and web browsers’ vulnerabilities. Work was informed by practical input from large industrial partners in EU-funded DiSIEM project (2016-2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
