You are probably not the weakest link: Towards practical prediction of susceptibility to semantic social engineering attacks
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16130
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2016.2616285
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 6910
- Volume
- 4
- Issue
- UNSPECIFIED
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- 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
-
2
- Research group(s)
-
-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was the first to identify factors of susceptibility to phishing and other social engineering attacks that can be measured automatically, ethically and in real-time. As such, it has allowed the development of CogniSense, a Windows application that monitors in real-time the likelihood that a user will recognise such an attack should it occur. This work was adopted as a case study and was extended for the purpose of situated cyber security training for social engineering attacks by the University of Kent in their EPSRC project ACCEPT.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
