学习关联：MAC地址随机化下BLE设备的自动重识别
Learning to Link: Automatic Re-identification of BLE Devices Under MAC Address Randomisation
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中文总结 AI 辅助
本研究利用机器学习自动生成签名，在MAC地址随机化下通过广播层特征实现BLE设备重识别，验证了标准监督学习方法的可行性。
中文摘要 AI 辅助
蓝牙低功耗（BLE）通过可解析私有地址（RPA）采用MAC地址随机化，以减轻公共广播信道上的长期设备跟踪。现有研究表明，广播数据包包含元数据和结构特征，可通过手工制定的规则重新识别目标设备。在本工作中，我们研究了在MAC随机化情况下，利用机器学习算法生成签名，自动化跟踪BLE设备过程的可行性。基于目标设备的实际蓝牙流量，我们刻画了广播层特征在RPA变化间的持久性，并将设备关联表述为一个监督分类问题。使用简单决策树分类器作为可行性证明方法，我们评估了在不同地址轮换模式下目标与非目标设备的可区分性。我们的结果强化了先前工作，表明广播层元数据能够在MAC随机化下实现设备重识别，以至于这种关联可以使用标准监督学习技术自动化完成，而无需任何特定技术知识。
英文摘要
Bluetooth Low Energy (BLE) employs MAC address randomisation -- via Resolvable Private Address (RPA) -- to mitigate long-term device tracking on public advertising channels. Existing research has shown that advertising packets contain metadata and structural features that allow re-identifying a target device via manually crafted rules. In this work, we investigate the feasibility of automating the process of tracking BLE devices despite MAC randomisation by leveraging machine learning algorithms for the signature creation. Based on the actual Bluetooth traffic from target devices, we characterise the persistence of advertising-layer features across RPA changes and formulate device linkage as a supervised classification problem. Using simple decision tree classifiers as a proof-of-feasibility approach, we evaluate the distinguishability of target and non-target devices under varying address rotation patterns. Our results reinforce prior work demonstrating that advertising-layer metadata can enable device re-identification under MAC randomisation, to the point where such linkage can be automated using standard supervised learning techniques, without any specific knowledge of the technology.
发表机构
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
- Politecnico di Torino（都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。
