GraphBAD : A General Technique for Anomaly Detection in Security Information and Event Management
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 35
- Type
- D - Journal article
- DOI
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10.1002/cpe.4433
- Title of journal
- Concurrency Computation Practice and Experience
- Article number
- e4433
- First page
- -
- Volume
- 30
- Issue
- 16
- ISSN
- 1532-0626
- Open access status
- Compliant
- Month of publication
- January
- 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
- Identifying potential weaknesses in security configurations is a significant challenge to computer security. An output of collaboration with IBM Research (co-author: Shirin), this research extends the usability of detection tools from the expert user to the general user, where expertise is not readily available (or affordable) for auditing their system's configuration. The research has led to (i) funding via Innovate UK’s Cyber Security Academic Startup Scheme (Grant Refs: 9644, 14636, and 18652) (ii) further IBM-Huddersfield collaboration e.g. co-organisation of SPARK workshops https://icaps19.icaps-conference.org/workshops/SPARK/index.html. Researchgate shows the article has generated hundreds of reads https://www.researchgate.net/publication/322781259_GraphBAD_A_general_technique_for_anomaly_detection_in_security_information_and_event_management.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
