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，也引发了人们对安全和隐私领域的重大关注。特别是，身份验证过程作为信息安全的第一道防线，通过验证个人或设备的身份，已经变得越来越关键。未经授权的访问可能会在保密损失和隐私泄露方面对公司和个人造成不利影响。与许多现有的关于用户/设备认证的研究不同，这些研究要么使用专门的或昂贵的硬件来安装和校准，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认证解决方案尤其具有吸引力，可以有效地补充传统的安全方法。由于在无处不在的无线环境中具有丰富的无线连接和独特的信号特性，该项目采取了不同的观点，利用无线网络中独特的物理属性来促进人类和移动设备的隐式身份验证。这项研究可以促进我们利用无线网络中的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。它还可以增进我们在开发深度学习技术方面的理解，以根据人们在物理环境中的活动来验证他们的身份。此外，教育努力包括课程开发，K-12和本科参与，以及学生参与研究的代表性不足。该项目专注于建立一个整体框架，利用商业无线网络提供的细粒度无线电信号来执行隐含的用户/设备身份验证。该框架旨在为在无线网络中集成细粒度物理特性以增强无线安全奠定基础。研究表明，无线网络中的细粒度信号特性能够捕捉到人类在静止和移动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，并提取能够区分个体的独特特征。它进一步开发了深度学习技术，以根据人们在物理环境中的日常活动对其进行身份验证。身份验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，以促进采用广泛的防御策略。

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