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This research addresses the critical vulnerability of Radio Frequency Fingerprinting (RFF) systems to thermal fluctuations, proposing a novel machine learning framework that integrates temperature data directly into the authentication model. By explicitly accounting for environmental and internal heat variations, this approach significantly enhances classification accuracy and robustness against spoofing attempts in real-world BLE scenarios. The work underscores the necessity of incorporating physical layer variables like device temperature as essential features to ensure reliable hardware-based security protocols.
Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy
arXiv:2607.25070v1 Announce Type: cross Abstract: Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals. Yet existing methods largely overlook a major drawback: RFFP sensitivity to temperature--a critical factor influenced by both internal and environmental conditions--which can significantly alter device signatures and degrade classification performance. In this paper, we propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process to improve robustness and generalization. We evaluate the proposed method on a real-world Bluetooth Low Energy (BLE) dataset collected across multiple devices and environmental conditions. Experimental results demonstrate that temperature-aware modeling consistently outperforms other temperature mitigation baselines, achieving significant improvements in classification accuracy, particularly under unseen temperature and environmental conditions.
Abstract:Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals. Yet existing methods largely overlook a major drawback: RFFP sensitivity to temperature--a critical factor influenced by both internal and environmental conditions--which can significantly alter device signatures and degrade classification performance. In this paper, we propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process to improve robustness and generalization. We evaluate the proposed method on a real-world Bluetooth Low Energy (BLE) dataset collected across multiple devices and environmental conditions. Experimental results demonstrate that temperature-aware modeling consistently outperforms other temperature mitigation baselines, achieving significant improvements in classification accuracy, particularly under unseen temperature and environmental conditions.
6 pages, 11 figures. Accepted at 17th International Conference on Network of the Future (NoF2026)
Signal Processing (eess.SP); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2607.25070 [eess.SP]
(or arXiv:2607.25070v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2607.25070 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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