Peer-reviewedCC BYMethodsX
Adaptive edge-cloud isolation forest-autoencoder pipeline in real-time with delta policy feedback for zero-day IoT smartwatch intrusion detection
- 1School of Computer Science and Applications, REVA University, Bengaluru, India
Related
- XGB-AGMoE: A Validation-Adaptive XGBoost-Anchored Granular Mixture-of-Experts Framework for Multi-Class Intrusion Detection in IoMT WiFi–MQTT Traffic
- A temperature-adaptive logit-standardized knowledge distillation for lightweight network intrusion detection
- Adaptive active learning and metaheuristic-optimized transformer framework for robust intrusion detection in software-defined networks
- An adaptive contextual memory controller based long short-term memory (ACMC-LSTM) for intrusion detection in network traffic
- AFI-ID: adaptive feature intelligence for intrusion detection
BibTeX
@article{nagashree2026adaptive,
title = {{Adaptive edge-cloud isolation forest-autoencoder pipeline in real-time with delta policy feedback for zero-day IoT smartwatch intrusion detection}},
author = {B.A. Nagashree and M. Vinayaka Murthy},
journal = {MethodsX},
year = {2026},
month = oct,
url = {https://europepmc.org/article/PMC/PMC13672837},
note = {CC BY},
}
RIS
TY - JOUR
TI - Adaptive edge-cloud isolation forest-autoencoder pipeline in real-time with delta policy feedback for zero-day IoT smartwatch intrusion detection
AU - B.A. Nagashree
AU - M. Vinayaka Murthy
PY - 2026
DA - 2026/10/01
JO - MethodsX
UR - https://europepmc.org/article/PMC/PMC13672837
SN - 2215-0161
ER -