Explainable Intrusion and Anomaly Detection for IoT Sensor Networks Using Hybrid Feature Selection and Deep Autoencoder Learning
- DOI
- 10.3390/s26144540
- Published
- 2026-07-17
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s26144540,
title = {Explainable Intrusion and Anomaly Detection for IoT Sensor Networks Using Hybrid Feature Selection and Deep Autoencoder Learning},
author = {Usman Ahmed and Sadiq Muhammad and Jaeyoung Choi},
year = {2026},
journal = {Sensors},
doi = {10.3390/s26144540},
url = {https://doi.org/10.3390/s26144540}
}RIS
TY - JOUR TI - Explainable Intrusion and Anomaly Detection for IoT Sensor Networks Using Hybrid Feature Selection and Deep Autoencoder Learning AU - Usman Ahmed AU - Sadiq Muhammad AU - Jaeyoung Choi PY - 2026 JO - Sensors DO - 10.3390/s26144540 UR - https://doi.org/10.3390/s26144540 ER -
APA
Ahmed, U., Muhammad, S., & Choi, J. (2026). Explainable Intrusion and Anomaly Detection for IoT Sensor Networks Using Hybrid Feature Selection and Deep Autoencoder Learning. Sensors. https://doi.org/10.3390/s26144540
Source records
- crossref · retrieved 2026-09-26T19:53:58.317Z