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: 1717356
• 负责人: Xiaonan Guo
• 金额: $ 16万
• 依托单位: Indiana University
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717356&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，也引起了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第一道防线的身份验证过程变得越来越重要。未经授权的访问可能会对公司和个人造成机密丢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，新兴的低成本且不引人注目的无需用户参与的身份验证解决方案对于有效地实现身份验证特别有吸引力。补充传统的安全方法。由于无线环境中丰富的无线连接和独特的信号特征，该项目采取了不同的观点，通过利用无线网络中独特的物理属性来促进对人类和移动设备的隐式身份验证。拟议的研究可以提高我们在利用无线网络中的物理层信息来捕获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，以根据人们在物理环境中的活动对他们进行身份验证。此外，教育工作包括课程开发、K-12 和本科生参与，以及代表性不足的学生参与研究。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/设备验证。所提出的框架旨在为无线网络中集成细粒度物理属性奠定基础，以增强无线安全性。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日常活动中独特的生理和行为特征。所提出的框架对无线信号进行智能分割，并提取独特的特征，从而能够区分个体。它进一步开发深度学习技术，根据人们在物理环境中的日常活动对他们进行身份验证。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，以促进广泛的防御策略的采用。

项目成果

Mobile Device Usage Recommendation based on User Context Inference Using Embedded Sensors

使用嵌入式传感器基于用户上下文推断的移动设备使用推荐

• DOI: 10.1109/icccn49398.2020.9209697
• 发表时间: 2020-08-01
• 期刊: 2020 29th International Conference on Computer Communications and Networks (ICCCN)
• 作者: Cong Shi;Xiaonan Guo;Ting Yu;Yingying Chen;Yucheng Xie;Jian Liu
• 通讯作者: Jian Liu

MU-ID: Multi-user Identification Through Gaits Using Millimeter Wave Radios

MU-ID：使用毫米波无线电通过步态进行多用户识别

• DOI: 10.1109/infocom41043.2020.9155471
• 发表时间: 2020-07-01
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: X. Yang;Jian Liu;Yingying Chen;Xiaonan Guo;Yucheng Xie
• 通讯作者: Yucheng Xie

Environment-independent In-baggage Object Identification Using WiFi Signals

使用 WiFi 信号进行独立于环境的行李内物体识别

• DOI: 10.1109/mass52906.2021.00018
• 发表时间: 2021-10-01
• 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
• 通讯作者: Yingying Chen

WiFi-Enabled Smart Human Dynamics Monitoring

支持 WiFi 的智能人体动态监测

• DOI: 10.1145/3131672.3131692
• 发表时间: 2017-11-01
• 期刊: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
• 作者: Xiaonan Guo;Bo Liu;Cong Shi;Hongbo Liu;Yingying Chen;M. Chuah
• 通讯作者: M. Chuah

Poster: Video Chat Scam Detection Leveraging Screen Light Reflection

海报：利用屏幕光反射检测视频聊天诈骗

• DOI: 10.1145/3300061.3343403
• 发表时间: 2019-10
• 期刊: The 25th Annual International Conference on Mobile Computing and Networking
• 作者: Liu, Hongbo;Li, Zhihua;Xie, Yucheng;Jiang, Ruizhe;Wang, Yan;Guo, Xiaonan;Chen, Yingying
• 通讯作者: Chen, Yingying