Collaborative Research: SaTC: CORE: Medium: Toward Age-Aware Continuous Authentication on Personal Computing Devices

当前的用户认证软件依赖于用户直接发起一些交互（即主动认证）。然而，并非所有年龄组的个人都可以使用主动身份验证系统。连续身份验证方案透明地观察用户的自然多模态行为，以利用所有可能的信号作为身份验证的输入，因此不需要用户发起显式的身份验证交互，因此是一个很有前途的框架，适用于不同年龄段的个人进行身份验证。本项目的新颖之处在于：1)促进对不同年龄组的个体如何使用和感知现有认证方法的理解，特别是关于用户的心理模型和对持续认证目的的监控的接受程度；2)收集和分析多种行为和生理模式的各种用户信号，用于个人计算设备上的年龄感知持续认证。这项研究还为其他环境（如公共空间和其他智能环境）中连续身份验证交互的设计提供了信息，在这些环境中，连续身份验证可能是有用的。本研究分为三个阶段。(1)引出多代用户对系统认证意味着什么的心理模型，他们是否以及何时期望系统在他们继续交互时重新认证他们以确认他们的身份，以及他们是否以及如何期望接收认证尝试的反馈。(2)通过一系列的数据收集环节，招募不同年龄段的个体完成一系列不同的任务，生成一个新的行为和生理数据数据集，如触摸手势、按键动力学、心率变异性和皮肤温度。(3)通过使用最先进的机器和深度学习技术分析这些数据，发展年龄感知连续认证的基础知识。该项目通过让STEM中代表性不足的学生参与进来，并通过K-12外展，扩大了对计算机的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Current software for user authentication relies on the user to directly initiate some interaction (i.e., active authentication). However, active authentication systems are not accessible to individuals across all age groups. Continuous authentication schemes transparently observe a user's natural multimodal behaviors to leverage all possible signals as input for authentication, and hence do not require explicit authentication interactions to be initiated by the user, and are thus a promising framework for authentication by individuals of different age groups. This project's novelties are 1) to advance understanding of how individuals of different age groups use and perceive existing authentication methods, especially concerning users' mental models and acceptance of monitoring for the purposes of continuous authentication, and 2) to collect and analyze a variety of user signals in multiple behavioral and physiological modalities for age-aware continuous authentication on personal computing devices. This research also informs the design of continuous authentication interactions in other contexts such as public spaces and other smart environments, in which continuous authentication might be useful. The research includes three phases. (1) Elicit the mental models multi-generational users have of what it means to authenticate to a system, if and when they expect the system to re-authenticate them to confirm their identity as they continue to interact, and if and how they expect to receive feedback of authentication attempts. (2) Produce a novel dataset of behavioral and physiological data, such as touch gestures, keystroke dynamics, heart-rate variability, and skin temperature, through a series of data collection sessions wherein individuals of different age groups will be recruited to complete a diverse set of tasks. (3) Develop fundamental knowledge of age-aware continuous authentication through the analysis of these data using state-of-the-art machine and deep learning techniques. This project broadens participation in computing by involving students underrepresented in STEM and through K-12 outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.