High accuracy android malware detection using ensemble learning
- Submitting institution
-
De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11148
- Type
- D - Journal article
- DOI
-
10.1049/iet-ifs.2014.0099
- Title of journal
- IET Information Security
- Article number
- -
- First page
- 313
- Volume
- 9
- Issue
- 6
- ISSN
- 1751-8709
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- 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
- 73
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper won 2017 IET Information Security premium best paper award (measured on 2-year impact on the Journal). This work led to a PhD research project (from February 2015 to February 2019) at Queen’s University Belfast, to investigate the applicability of the proposed approach in dynamic analysis-based detection of highly obfuscated Android Malware.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -