An ensemble approach for imbalanced multiclass malware classification using 1D-CNN.
- DOI
- 10.7717/peerj-cs.1677
- Published
- 2023
- Container
- PeerJ. Computer science
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.7717/peerj-cs.1677,
title = {An ensemble approach for imbalanced multiclass malware classification using 1D-CNN.},
author = {Panda B and Bisoyi SS and Panigrahy S},
year = {2023},
journal = {PeerJ. Computer science},
doi = {10.7717/peerj-cs.1677},
url = {https://doi.org/10.7717/peerj-cs.1677}
}RIS
TY - JOUR TI - An ensemble approach for imbalanced multiclass malware classification using 1D-CNN. AU - Panda B AU - Bisoyi SS AU - Panigrahy S PY - 2023 JO - PeerJ. Computer science DO - 10.7717/peerj-cs.1677 UR - https://doi.org/10.7717/peerj-cs.1677 ER -
APA
B, P., SS, B., & S, P. (2023). An ensemble approach for imbalanced multiclass malware classification using 1D-CNN.. PeerJ. Computer science. https://doi.org/10.7717/peerj-cs.1677
Source records
- pubmed · retrieved 2026-09-25T02:32:51.424Z