BioTag: robust RFID-based continuous user verification using physiological features from respiration
BioTag: robust RFID-based continuous user verification using physiological features from respiration
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DOI：
10.1145/3492866.3549718
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
B. Hu;Tianming Zhao;Yan Wang;Jerry Q. Cheng;R. Howard;Yingying Chen;Hao Wan
中科院分区：
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作者：
B. Hu;Tianming Zhao;Yan Wang;Jerry Q. Cheng;R. Howard;Yingying Chen;Hao Wan
几十年来，一次性验证一直是入口处、办公室等处用户验证的标准。然而，这种方法要求用户提供其秘密（例如，输入密码和收集指纹）并手动重新验证（例如，关闭屏幕）。因此，他们无法在验证后确认用户是合法用户还是冒名顶替者，这就迫切需要一种更方便、更安全的解决方案来进行持续的用户验证。然而，现有的持续验证方法严重依赖用户的主动参与，不方便。为此，我们提出了一种持续的用户验证系统BioTag，它利用低成本的射频识别（RFID）技术来捕获植根于用户呼吸运动的独特生理特征，以进行持续的用户验证。具体来说，我们使用贴在用户胸部和腹部的两个 RFID 标签，通过 RFID 信号捕获用户的固有呼吸模式。我们开发基于波形形态分析和模糊小波变换（FWPT）的呼吸特征提取方法，从用户的呼吸信号中获取独特的生物识别信息。此外，我们使用梯度提升决策树（GBDT）开发了一个自适应分类器来准确识别合法用户和攻击者。涉及 41 名参与者的广泛实验表明，BioTag 可以可靠地验证用户身份并以较低的训练量检测各种类型的对手。特别是，我们的系统在随机攻击和模仿攻击场景下分别可以实现超过 95.2% 和 94.8% 的验证准确率。
For decades, one-time verification has been the standard for user verification at entry points, office rooms, etc. However, such approaches request users to provide their secrets (e.g., entering passwords and collecting fingerprints) and re-verify (e.g., screen shutdown) manually. Thus, they cannot confirm whether the user is a legitimate or an imposter after verification, which raises the urgent demand for a more convenient and secure solution to perform continuous user verification. However, existing continuous verification methods heavily rely on users' active participation, which is inconvenient. Toward this end, we propose a continuous user verification system, BioTag, which utilizes the low-cost radio frequency identification (RFID) technology to capture unique physiological characteristics rooted in the users' respiration motions for continuous user verification. Specifically, we use two RFID tags attached to a user's chest and abdomen to capture the user's intrinsic respiratory patterns via RFID signals. We develop respiratory feature extraction methods based on waveform morphology analysis and fuzzy wavelet transformation (FWPT) to derive unique biometric information from the user's respiration signals. Furthermore, we develop an adaptive classifier using the gradient boosting decision tree (GBDT) to identify legitimate users and attackers accurately. Extensive experiments involving 41 participants demonstrate that BioTag can robustly authenticate users and detect various types of adversaries with low training effort. In particular, our system can achieve over 95.2% and 94.8% verification accuracy on random attack and imitation attack scenarios, respectively.