Analysis of Bayesian classification‐based approaches for Android malware detection
- Submitting institution
-
De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11147
- Type
- D - Journal article
- DOI
-
10.1049/iet-ifs.2013.0095
- Title of journal
- IET Information Security
- Article number
- -
- First page
- 25
- Volume
- 8
- Issue
- 1
- ISSN
- 1751-8709
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- 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
- 69
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This (and the conference version) was the first paper to propose a machine learning-based approach with static analysis for Android malware detection. The paper is cited in US patent No. 8869277, granted to Microsoft corporation. The paper resulted in GCQH funded project (2013-2014) on Automated Detection of Android Malware that produced a malware classification engine using the paper’s results.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -