DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11149
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2017.2777960
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 453
- Volume
- 49
- Issue
- 2
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- 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
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1
- Research group(s)
-
-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper appears in one of the top ranked Computer Science Journals (IEEE Transactions). It proposes a novel multi-level classifier fusion method that outperforms conventional fusion methods. Its superior accuracy performance is demonstrated on Android malware and benign datasets created from large application repositories. The datasets have been publicly released on Figshare as ‘Malgenome-215-dataset’ and ‘Drebin-215-dataset’ and have >2,000 downloads. The datasets have been used and cited by other researchers for example in the Droid-NNet paper (10.1109/BigData47090.2019.9006053).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -