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给我们的日常生活带来了极大的便利，但也引起了人们对安全和隐私领域的重大关注。尤其是作为信息安全第一道防线的身份验证过程，其重要性日益凸显。未经授权的访问可能会对公司和个人造成保密损失和隐私泄露的不利影响。与许多现有的用户/设备身份验证研究不同，这些研究要么使用专业的或昂贵的硬件，需要专家进行安装和校准，要么需要用户的积极参与，而新兴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，可以有效地补充传统的安全方法。由于普及无线环境中丰富的无线连接和独特的信号特性，本项目采用不同的观点，利用无线网络中独特的物理特性来促进人和移动设备的隐式认证。该研究可以提高我们在利用无线网络物理层信息来捕捉人类在日常活动中独特的生理和行为特征方面的知识。它还可以增强我们对开发深度学习技术的理解，根据人们在物理环境中的活动来验证他们的身份。此外，教育方面的努力包括课程开发，K-12和本科生的参与，以及代表性不足的学生对研究的参与。该项目侧重于构建一个整体框架，利用商业无线网络提供的细粒度无线电信号来执行隐式用户/设备认证。提出的框架旨在推进无线网络中细粒度物理特性集成的基础，以增强无线网络的安全性。研究表明，无线网络中的细粒度信号特性能够捕捉人类在固定和移动的日常活动中独特的生理和行为特征。该框架对无线信号进行智能分割，并提取独特的特征，使其具有区分个体的能力。它进一步开发了深度学习技术，根据人们在物理环境中的日常活动来验证他们的身份。认证过程不需要用户积极参与，也不需要用户佩戴任何设备。该项目还开发了有效的技术来检测用户欺骗的存在和定位攻击者，以促进广泛的防御策略的使用。