EAGER: Towards A Lightweight and Personalized Implicit Authentication System with Adaptive Sensing

EAGER：迈向具有自适应传感的轻量级、个性化隐式身份验证系统

基本信息

批准号：
1642590
负责人：
Jinyuan Stella Sun
金额：
$ 20万
依托单位：
University of Tennessee Knoxville
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642590&HistoricalAwards=false
关键词：
EAGER Towards Lightweight Personalized Implicit

项目摘要

Hard biometrics-based authentication methods such as face recognition, touch ID and iris scan require explicit action from users and expensive hardware. In contrast, behaviors are soft biometrics suitable for implicit and cost-efficient user authentication. This authentication method can lead to improved security by reducing the risk of users bypassing ill-designed authentication. It also relieves users from the burden of memorizing passwords, revolutionarily enhancing user experience. However, the fact that behaviors can change with many factors such as age, mood, and environment renders it more challenging to develop systems that depend on behaviors. This research project focuses on designing the first implicit authentication system that automatically selects dynamic sets of activities for user behavior extraction, and building a prototype authentication system. To ensure practical deployment, lightweight computation and adaptive sensing are integrated into the system, making it energy-efficient for popular energy-constraint devices.The proposed research project is an exploratory project in its early stages. One of the key contributions and unique aspects of the proposed research is that it sets the foundation for a cutting-edge engineering system that is still in its infancy and whose complexity extends far beyond a few novel algorithms. The most hindering factor of behavior-based implicit authentication systems is the difficulties in handling dynamic behavior changes that make the system unreliable. This project takes a drastically different approach by selecting the most suitable set of behaviors over time from available behaviors, and is thus a general framework that is device-independent. Three research tasks will be carried out: Modeling and selecting the dynamically changing behaviors unique for each person based on probabilistic models; Developing an energy-efficient and practical client side by solving challenging issues such as differentiating legitimate users behavior deviation from illegitimate users; Building a prototype authentication system to handle authentication failures and enhance user experience. The proposed research is potentially transformative. Exploring behaviors, activity patterns and habits serves as the basis for dynamic, intelligent, versatile, seamless and user-friendly designs, transforming modern computer systems and applications into our smarter assistants, educators, health watchers, security vaults, entertainers, play mates and many more. The proposed project has developed viable methods to capture users evolving behaviors, dynamically adjust the models, and abstract system components so that the generic system can be easily reusable and extensible. The research findings will contribute to advancing the state-of-the-art research, and stimulating the wide adoption of implicit authentication systems in various domains including cybersecurity, healthcare and fitness, mobile social networks, and e-commerce.

基于硬生物识别的身份验证方法，例如面部识别，触摸ID和IRIS扫描需要用户的明确操作和昂贵的硬件。相反，行为是软性生物识别技术，适用于隐式和成本效益的用户身份验证。这种身份验证方法可以通过降低用户绕过未设计的身份验证的风险来改善安全性。它还使用户免于记忆密码的负担，革命性的增强了用户体验。但是，行为可以随着年龄，情绪和环境等许多因素而变化的事实使发展依赖行为的系统更具挑战性。该研究项目的重点是设计第一个隐式身份验证系统，该系统自动为用户行为提取的动态活动集并构建原型身份验证系统。为了确保实际部署，将轻量级计算和自适应感应集成到系统中，从而使其用于流行的能源构成设备的能源有效。拟议的研究项目是其早期阶段的探索性项目。拟议的研究的主要贡献和独特的方面之一是，它为仍处于起步阶段且复杂性远远超过几种新型算法的尖端工程系统奠定了基础。基于行为的隐式身份验证系统最阻碍的因素是处理动态行为变化的困难，这使系统不可靠。该项目通过从可用的行为中选择最合适的行为来采用截然不同的方法，因此是一个无独立的通用框架。将执行三项研究任务：基于概率模型，为每个人的独特行为建模和选择动态变化的行为；通过解决诸如合法用户的行为偏差与非法用户的行为偏差之类的挑战性问题，开发节能和实用的客户方面；构建原型身份验证系统来处理身份验证失败并增强用户体验。拟议的研究具有潜在的变革性。探索行为，活动模式和习惯是动态，智能，多功能，无缝和用户友好设计的基础，将现代的计算机系统和应用程序转换为我们智能的助手，教育工作者，健康观察者，安全保险箱，安全库，娱乐者，娱乐者，伴侣等。拟议的项目开发了可行的方法，可以捕获用户不断发展的行为，动态调整模型和抽象系统组件，以便可以轻松地重复使用且可扩展通用系统。研究结果将有助于推进最先进的研究，并刺激各种领域中隐性身份验证系统的广泛采用，包括网络安全，医疗保健和健身，移动社交网络和电子商务。