Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Novel K-neighbors and gradient boosting based transfer features for optimizing network attack detection
Related
- Dual-layer edge intelligence for secure real-time medical IoT: a Zero-Trust sequential framework integrating soft-voting intrusion detection and Histogram Gradient Boosting
- Hybrid rank fusion of a group-token transformer with gradient boosting improves detection of evasive and novel-family malware
- Comparative analysis of gradient boosting and random forest regressors for nutrient prediction in smart agriculture
- Frozen sequence encoders with gradient boosting match the state of the art on drug–target affinity domain generalization, and a sequence defect in the Davis kinase benchmark
- A robust and reliable method for malware classification using hybrid features and gradient boosting
BibTeX
@article{raza2026novel,
title = {{Novel K-neighbors and gradient boosting based transfer features for optimizing network attack detection}},
author = {Ali Raza and M. Nauman Hassan and Amber Malik and Ibrar Hussain and Elizabeth Caro Montero and Eduardo Silva Alvarado and Imran Ashraf},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-75927-w},
url = {https://www.nature.com/articles/s41598-026-75927-w},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Novel K-neighbors and gradient boosting based transfer features for optimizing network attack detection
AU - Ali Raza
AU - M. Nauman Hassan
AU - Amber Malik
AU - Ibrar Hussain
AU - Elizabeth Caro Montero
AU - Eduardo Silva Alvarado
AU - Imran Ashraf
PY - 2026
DA - 2026/10/15
JO - Scientific Reports
DO - 10.1038/s41598-026-75927-w
UR - https://www.nature.com/articles/s41598-026-75927-w
SN - 2045-2322
ER -