Peer-reviewedCC BYSensors (Basel, Switzerland)
XGB-AGMoE: A Validation-Adaptive XGBoost-Anchored Granular Mixture-of-Experts Framework for Multi-Class Intrusion Detection in IoMT WiFi–MQTT Traffic
- 1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
- 2Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia;
- 3Information Systems & Technology Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt;
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BibTeX
@article{alruwaili2026xgbagmoe,
title = {{XGB-AGMoE: A Validation-Adaptive XGBoost-Anchored Granular Mixture-of-Experts Framework for Multi-Class Intrusion Detection in IoMT WiFi–MQTT Traffic}},
author = {Madallah Alruwaili and Fawaz J. Alruwaili and Mahmood Mohamed},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = sep,
url = {https://europepmc.org/article/PMC/PMC13667514},
note = {CC BY},
}
RIS
TY - JOUR
TI - XGB-AGMoE: A Validation-Adaptive XGBoost-Anchored Granular Mixture-of-Experts Framework for Multi-Class Intrusion Detection in IoMT WiFi–MQTT Traffic
AU - Madallah Alruwaili
AU - Fawaz J. Alruwaili
AU - Mahmood Mohamed
PY - 2026
DA - 2026/09/25
JO - Sensors (Basel, Switzerland)
UR - https://europepmc.org/article/PMC/PMC13667514
SN - 1424-8220
ER -