Robust Continuous Authentication Using Cardiac Biometrics From Wrist-Worn Wearables
Robust Continuous Authentication Using Cardiac Biometrics From Wrist-Worn Wearables
复制标题
DOI：
10.1109/jiot.2021.3128290
复制
发表时间：
2022-06
影响因子：
10.6
通讯作者：
Tianming Zhao;Yan Wang;Jian Liu;Jerry Q. Cheng;Yingying Chen;Jiadi Yu
中科院分区：
文献类型：
--
作者：
Tianming Zhao;Yan Wang;Jian Liu;Jerry Q. Cheng;Yingying Chen;Jiadi Yu
当对手可以在用户初次登录后获得未经授权的权限时，传统的一次性用户身份验证很容易受到攻击。连续用户认证（Continuous user authentication，CA）技术通过在很少用户参与的情况下实现无缝用户认证，显示出巨大的潜力。我们设计了一个低成本的系统，可以利用用户的脉动信号从光电容积脉搏波（PPG）传感器在商品可穿戴设备执行CA。我们的系统需要零用户的努力，并适用于实际情况下，具有非临床PPG测量与人体运动伪影（MA）。我们探索了人类心脏系统的独特性，并开发了自适应MA滤波方法，以减轻日常生活中短暂和连续活动的影响。此外，我们确定一般的基准功能，并开发一个自适应分类器，可以验证用户不断根据他们的心脏特征，很少有额外的训练工作。在实际场景下，20名参与者使用我们的腕戴式PPG传感平台进行的实验表明，我们的系统在检测随机攻击时可以达到90%以上的高CA准确率和4%的低误检率。我们表明，我们的MA缓解方法可以提高CA的准确性约39%，在瞬态和连续的日常活动的情况下。
Traditional one-time user authentication is vulnerable to attacks when an adversary can obtain unauthorized privileges after a user’s initial login. Continuous user authentication (CA) has recently shown its great potential by enabling seamless user authentication with few users’ participation. We devise a low-cost system that can exploit users’ pulsatile signals from photoplethysmography (PPG) sensors in commodity wearable devices to perform CA. Our system requires zero user effort and applies to practical scenarios that have nonclinical PPG measurements with human motion artifacts (MAs). We explore the uniqueness of the human cardiac system and develop adaptive MA filtering methods to mitigate the impacts of transient and continuous activities from daily life. Furthermore, we identify general fiducial features and develop an adaptive classifier that can authenticate users continuously based on their cardiac characteristics with little additional training effort. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks. We show that our MA mitigation approaches can improve the CA accuracy by around 39% under both transient and continuous daily activity scenarios.