Ransomware deployment methods and analysis: views from a predictive model and human responses
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14467
- Type
- D - Journal article
- DOI
-
10.1186/s40163-019-0097-9
- Title of journal
- Crime Science
- Article number
- 2
- First page
- -
- Volume
- 8
- Issue
- 1
- ISSN
- 2193-7680
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
https://kar.kent.ac.uk/71720/
- 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
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- To improve detection and handling of general ransomware incidents, we investigated a significant number of strains, leading to a model and proof-of-concept tool for categorising behavioural characteristics. This is significant because it provides a technical approach based on characterising Windows API calls and their traits which is combined with a user study, including real victims. The accompanying tool Github repository (given the the paper) has been forked twice and received five stars.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
