Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions
- DOI
- 10.1038/s41598-026-43745-9
- Published
- 2026-04-28
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-43745-9,
title = {Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions},
author = {Rijvan Beg and Nikhil Nigam and Yogesh Kumar Sharma and Amit Patel and Surendra Solanki and Sukhwinder Sharma and Lalit Kumar},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-43745-9},
url = {https://doi.org/10.1038/s41598-026-43745-9}
}RIS
TY - JOUR TI - Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions AU - Rijvan Beg AU - Nikhil Nigam AU - Yogesh Kumar Sharma AU - Amit Patel AU - Surendra Solanki AU - Sukhwinder Sharma AU - Lalit Kumar PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-43745-9 UR - https://doi.org/10.1038/s41598-026-43745-9 ER -
APA
Beg, R., Nigam, N., Sharma, Y. K., Patel, A., Solanki, S., Sharma, S., & Kumar, L. (2026). Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions. Scientific Reports. https://doi.org/10.1038/s41598-026-43745-9
Source records
- crossref · retrieved 2026-09-25T11:55:32.993Z