发表机构
Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究射频指纹识别受设备温度影响的问题，提出温度感知RFFP框架，将温度信息纳入学习过程，在真实BLE数据集上评估，该方法优于其他基线，提升了分类准确性，尤其在未见温度和环境条件下。
AI 中文摘要
射频指纹识别（RFFP）作为一种利用发射信号中嵌入的硬件特定损伤进行设备认证的方法已崭露头角。但现有方法大多忽视了一个主要缺点：RFFP对温度敏感，而温度受内部和环境条件影响，会显著改变设备特征并降低分类性能。本文提出一种新颖的温度感知RFFP框架，将设备温度信息明确纳入学习过程以提高鲁棒性和泛化能力。我们在跨多个设备和环境条件收集的真实世界蓝牙低功耗（BLE）数据集上评估该方法。实验结果表明，温度感知建模始终优于其他温度缓解基线，在分类准确性上有显著提高，尤其是在未见温度和环境条件下。
英文摘要
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.
Comments6 pages, 11 figures. Accepted at 17th International Conference on Network of the Future (NoF2026)
