The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their personal uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors which enable to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication, for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. Performance evaluation of selected machine learning algorithms shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.

中文翻译：

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您的智能手环是否足够聪明以了解您的身份：野外持续的生理验证

使用处理隐私敏感信息（例如数字银行、普及医疗、智能家居应用）的云服务需要隐式连续身份验证解决方案，这将使这些系统不易受到欺骗攻击。生理信号由于其个人独特性，可用于连续认证。无处不在的腕戴式可穿戴设备配备了能够提取心率变异性 (HRV) 特征的光体积描记图传感器。在本研究中，我们展示了这些设备可用于持续的生理身份验证，以增强云、边缘服务和物联网设备的安全性。适用于智能带框架的系统面临着新的挑战，例如相对较低的信号质量和由于放置导致的伪影，而这些都是在全导联心电图系统中没有遇到的。去除伪影后，将清洁后的生理信号馈送到机器学习算法。为了训练我们的机器学习模型，我们在现实生活中使用现成的智能手环和智能手表收集生理数据。所选机器学习算法的性能评估表明 HRV 是连续不显眼的隐式生理认证的有力候选者。我们在现实生活中使用现成的智能手环和智能手表收集生理数据。所选机器学习算法的性能评估表明 HRV 是连续不显眼的隐式生理认证的有力候选者。我们在现实生活中使用现成的智能手环和智能手表收集生理数据。所选机器学习算法的性能评估表明 HRV 是连续不显眼的隐式生理认证的有力候选者。