Proactive threat detection for connected cars using recursive Bayesian estimation
- Submitting institution
-
University of Wolverhampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1260
- Type
- D - Journal article
- DOI
-
10.1109/JSEN.2017.2782751
- Title of journal
- IEEE Sensors Journal
- Article number
- -
- First page
- 4822
- Volume
- 18
- Issue
- 12
- ISSN
- 1530-437X
- Open access status
- Access exception
- Month of publication
- December
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A Bayesian estimation technique was developed to predict future states and cyber-threats while minimising reaction time when encountering anomalies. This research was used to offer innovative cybersecurity training for the SDCDC-19 programme by the CyberSafe foundation, and funded by UK Aid and FCO; catered to 3057 employees from 1504 companies located in 35 states across Nigeria, 40.7% of which were female led/owned. This research was invited for a presentation in Forensics Europe Expo at London Olympia in Mar 2019 and also invited to have its technical details extended and published in the digital forensics magazine (http://www.digitalforensicsmagazine.com/index.php?option=com_content&view=article&id=1290&Itemid=99).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -