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This paper addresses the vulnerability of Radio Frequency Fingerprinting (RFFP) to temperature variations, which can significantly degrade device classification performance. By introducing a temperature-aware RFFP framework that integrates temperature data into the learning process, the authors enhance the robustness and generalization of device authentication. Experimental results on a diverse Bluetooth Low Energy dataset show that this approach outperforms existing temperature mitigation techniques, leading to substantial gains in classification accuracy under varied conditions.
Temperature-aware modeling boosts RFFP classification accuracy by addressing a critical vulnerability that has been largely ignored in prior research.
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.