CloudIntell: An intelligent malware detection system
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185741840
- Type
- D - Journal article
- DOI
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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
- July
- 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
- 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, CloudIntell, which exploits machine learning in order to enhance malware detection rate whilst improving energy efficiency. It analyses practical applications, e.g., Intel Corp’s report shows that they detect more than 7000 malware attacks every hours for mobile devices. This number can be significantly higher if other devices are counted such as individual computers, enterprise networks, and websites. This research builds on collaboration between OBU and Bradford. This work inspired two subsequent papers which are published in the IEEE flagship conferences, GLOBECOM 2018 and GLOBECOM 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -