@inproceedings{0a9257a100684ce28b127896230f60d6,
title = "Machine Learning Models for LoRa Wan IoT Anomaly Detection",
abstract = "LoRaWAN provides a long-range communication among IoT devices. Since a LoRaWAN gateway becomes a bridge between LoRaWAN nodes and back-end server, it could has potential security risks. We present an anomaly detection system to secure LoRa Wangateway devices by evaluating incoming packet data. To evaluate our proposed system, we build machine learning models using various outlier detection algorithms. We construct and evaluate LoRaWAN dataset from LoRaWAN gateway devices. The simulation and experimental results show that machine learning to address anomaly detection on constrained LoRa Wandevices guarantees feasibility, accu-racy and performance.",
keywords = "anomaly detection, LoRaWAN, machine learning, security system",
author = "Agus Kurniawan and Marcel Kyas",
note = "Funding Information: VII. ACKNOWLEDGEMENTS This work was partially supported by the Directorate General of Higher Education (DGHE) of Indonesia. Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2022 ; Conference date: 01-10-2022 Through 03-10-2022",
year = "2022",
doi = "10.1109/ICACSIS56558.2022.9923439",
language = "English",
series = "Proceedings - ICACSIS 2022: 14th International Conference on Advanced Computer Science and Information Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "193--198",
booktitle = "Proceedings - ICACSIS 2022",
address = "United States",
}