CloudIntell: An intelligent malware detection system
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22
- Type
- D - Journal article
- DOI
-
10.1016/j.future.2017.07.016
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 1042
- Volume
- 86
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0167739X17314929?via%3Dihub
- 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
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work develops a new methodology and system (called CloudIntell) which exploits machine learning techniques in order to enhance malware detection rate whilst improving energy efficiency. This work exerts influence on practical applications. For instance, a recent report from Intel shows that they detect more than 7000 malware attacks every hour for mobile devices. This research builds on collaboration among Gloucestershire, Bradford and Oxford Brookes universities and is leading to preparation of a research funding application.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
