SaTC: CORE: Small: Collaborative: Exploiting Physical Properties in Wireless Networks for Implicit Authentication

SaTC：核心：小型：协作：利用无线网络中的物理属性进行隐式身份验证

• 批准号: 1716500
• 负责人: Yingying Chen
• 金额: $ 34万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1716500&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Collaborative; Exploiting

The rapid development of information technology not only leads to great convenience in our daily lives, but also raises significant concerns in the field of security and privacy. Particularly, the authentication process, which serves as the first line of information security by verifying the identity of a person or device, has become increasingly critical. An unauthorized access could result in detrimental impact on both corporation and individual in both secrecy loss and privacy leakage. Unlike many existing studies on user/device authentication, which either employ specialized or expensive hardware that needs experts for installation and calibration or require users' active involvement, the emerging low-cost and unobtrusive authentication solution without the users' participation is particularly attractive to effectively complement conventional security approaches. Due to the rich wireless connectivity and unique signal characteristics in pervasive wireless environments, this project takes a different view point by exploiting unique physical properties in wireless networks to facilitate implicit authentication for both human and mobile devices. The proposed research could advance our knowledge in exploiting the physical layer information in wireless networks to capture unique physiological and behavioral characteristics from human during their daily activities. It could also enhance our understanding in developing deep learning techniques to authenticate people based on their activities in the physical environments. Additionally, the educational efforts include curriculum development, K-12 and undergraduate involvement, and underrepresented student engagement in research.This project focuses on building a holistic framework that leverages fine-grained radio signals available from the commercial wireless networks to perform implicit user/device authentication. The proposed framework aims to advance the foundation of integrating fine-grained physical properties in wireless networks to enhance wireless security. The research reveals that the fine-grained signal properties in wireless networks are capable to capture unique physiological and behavioral characteristics from human in both stationary and mobile daily activities. The proposed framework develops smart segmentation on the wireless signals and extract unique features that enable the capability of distinguishing individual. It further develops deep learning techniques to authenticate people based on their daily activities in the physical environments. The authentication process does not require active user involvement nor require the user to wear any device. This project also develops efficient techniques to detect the presence of user spoofing and localize attackers to facilitate the employment of a broad array of defending strategies.

信息技术的快速发展不仅会在我们的日常生活中提供极大的便利，而且在安全和隐私领域引起了重大关注。特别是，通过验证人员或设备的身份作为第一条信息安全性的身份验证过程变得越来越重要。未经授权的访问可能会导致对公司和个人的保密损失和隐私泄漏的影响。与许多有关用户/设备身份验证的现有研究不同，这些研究使用专业或昂贵的硬件需要专家进行安装和校准，或者需要用户积极参与，而没有用户参与的新出现的低成本和不引人注目的身份验证解决方案特别有吸引力，这对于有效地补充常规安全方法特别有吸引力。由于无线连接性丰富和无线环境中的独特信号特性，该项目通过利用无线网络中的独特物理属性来促进人体和移动设备的隐式身份验证，从而取得了不同的观点。拟议的研究可以促进我们的知识来利用无线网络中的物理层信息，以在日常活动中捕获人类的独特生理和行为特征。它还可以增强我们在开发深度学习技术方面的理解，以根据他们在物理环境中的活动来验证人们。此外，教育努力包括针对研究，K-12和本科参与，以及代表不足的学生参与研究。该项目着重于建立一个整体框架，该框架利用商业无线网络可获得的精细广播信号来执行隐式用户/设备的验证。拟议的框架旨在提高整合无线网络中细粒物理特性以增强无线安全性的基础。该研究表明，无线网络中的细粒信号特性能够在固定和移动日常活动中捕获人类的独特生理和行为特征。所提出的框架在无线信号上开发了智能细分，并提取独特的功能，从而可以区分个人。它进一步发展了深度学习技术，以根据人们在物理环境中的日常活动来对其进行身份验证。身份验证过程不需要主动用户参与，也不需要用户佩戴任何设备。该项目还开发了有效的技术，以检测用户欺骗和本地化攻击者的存在，以促进雇用各种各样的防御策略。