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: 1820624
• 负责人: Yingying Chen
• 金额: $ 34万
• 依托单位: Rutgers University New Brunswick
• 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: 2017-10-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820624&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，而且也引起了人们对安全和隐私领域的重大关注。特别是，通过验证个人或设备的身份作为信息安全的第一道防线的认证过程变得越来越重要。未经授权的访问会对企业和个人造成保密损失和隐私泄露的不利影响。与许多现有的关于用户/设备认证的研究不同，这些研究要么采用专门或昂贵的硬件，需要专家进行安装和校准，要么需要用户的积极参与，新兴的低成本和不引人注目的认证解决方案在没有用户参与的情况下，对于有效地补充传统的安全方法特别有吸引力。由于普遍无线环境中丰富的无线连接性和独特的信号特征，该项目采取不同的观点，利用无线网络中独特的物理属性来促进人类和移动的设备的隐式身份验证。该研究可以进一步利用无线网络中的物理层信息来捕获人类在日常活动中的独特生理和行为特征。它还可以增强我们对开发深度学习技术的理解，以根据人们在物理环境中的活动对其进行身份验证。此外，教育工作包括课程开发，K-12和本科生的参与，并在research.This项目的重点是建立一个整体的框架，利用细粒度的无线电信号可从商业无线网络执行隐式用户/设备身份验证。该框架的目的是推进在无线网络中集成细粒度的物理属性，以提高无线安全的基础。研究表明，无线网络中的细粒度信号特性能够捕获人类在固定和移动的日常活动中的独特生理和行为特征。所提出的框架开发智能分割的无线信号，并提取独特的功能，使区分个人的能力。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对其进行身份验证。认证过程不需要用户主动参与，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，并定位攻击者，以促进广泛的防御策略的就业。

项目成果

Face-Mic: inferring live speech and speaker identity via subtle facial dynamics captured by AR/VR motion sensors

• DOI: 10.1145/3447993.3483272
• 发表时间: 2021-10
• 期刊: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking
• 作者: Cong Shi;Xiangyu Xu;Tianfang Zhang;Pa Walker;Yi Wu;Jian Liu;Nitesh Saxena;Yingying Chen;Jiadi Yu

Semi-black-box Attacks Against Speech Recognition Systems Using Adversarial Samples

• DOI: 10.1109/dyspan.2019.8935789
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
• 作者: Yi Wu;Jian Liu;Yingying Chen;Jerry Q. Cheng

Lip Reading-Based User Authentication Through Acoustic Sensing on Smartphones

通过智能手机上的声学传感进行基于唇读的用户身份验证

• DOI: 10.1109/tnet.2019.2891733
• 发表时间: 2019-02-01
• 期刊: IEEE-ACM TRANSACTIONS ON NETWORKING
• 影响因子: 3.7
• 作者: Lu, Li;Yu, Jiadi;Li, Minglu
• 通讯作者: Li, Minglu

Environment-independent In-baggage Object Identification Using WiFi Signals

• DOI: 10.1109/mass52906.2021.00018
• 发表时间: 2021-10
• 期刊: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Cong Shi;Tianming Zhao;Yucheng Xie;Tianfang Zhang;Yan Wang;Xiaonan Guo;Yingying Chen

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

• DOI: 10.1145/3307334.3326093
• 发表时间: 2019-05
• 期刊: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
• 作者: Jian Liu;Cong Shi;Yingying Chen;Hongbo Liu;M. Gruteser