Providing SIEM systems with self-adaptation
- Submitting institution
-
Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0057
- Type
- D - Journal article
- DOI
-
10.1016/j.inffus.2013.04.009
- Title of journal
- Information Fusion
- Article number
- -
- First page
- 145
- Volume
- 21
- Issue
- 1
- ISSN
- 1566-2535
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://dl.acm.org/doi/10.1016/j.inffus.2013.04.009
- 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
-
-
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- With 31 citations (up to January 2021) such as in ACM Computing Surveys June 2018 Article No. 55 https://doi.org/10.1145/3184898, this paper (https://doi.org/10.1016/j.inffus.2013.04.009) inspired and is still inspiring many others in the search of autonomous and efficient methods for cyber security data analytics. In particular the work https://doi.org/10.1016/j.future.2019.09.005 develops a method for knowledge extraction from large datasets of computer logs at a real-life system of a top leading company in the Air Traffic Control domain. In the year of publication (2015), the Elsevier Information Fusion Journal obtained a JCR4.353; currently the journal got a JCR13.669 of impact.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -