Machine Learning and Deep Learning-Based Multi-Attribute Physical-Layer Authentication for Spoofing Detection in LoRaWAN

Azita Pourghasem (), Raimund Kirner, Athanasios Tsokanos, Iosif Mporas () and Alexios Mylonas
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Azita Pourghasem: Cybersecurity and Computing Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Raimund Kirner: Cybersecurity and Computing Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Athanasios Tsokanos: Cybersecurity and Computing Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Iosif Mporas: Cybersecurity and Computing Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Alexios Mylonas: Cybersecurity and Computing Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

Future Internet, 2025, vol. 17, issue 2, 1-14

Abstract: The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands.

Keywords: wireless sensor networks; physical-layer authentication; deep learning; machine learning; spoofing; multi-attribute; altitude; radio frequency fingerprinting; battery level; RSSI; LoRaWAN (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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