CRII: CNS: RUI: Exploiting Robust Deep Learning Framework for Wireless Localization Systems in Adversarial IoT Environments

CRII：CNS：RUI：在对抗性物联网环境中利用强大的深度学习框架实现无线定位系统

• 批准号: 2105416
• 负责人: Xuyu Wang
• 金额: $ 17.5万
• 依托单位: University Enterprises, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105416&HistoricalAwards=false
• 关键词: CRII; CNS; RUI; Exploiting; Robust

With the proliferation of wireless networks and mobile devices, wireless Internet of Things (IoT) applications (e.g., location-based services) have gained considerable attention. Indoor localization faces a number of challenges in the radio propagation environment, including the multipath effect, shadowing, fading, and delay distortion. To tackle the non-line-of-sight (NLOS) indoor environment, fingerprinting based wireless localization methods using deep neural networks (DNN) have been proposed. However, a data-driven only approach using DNN may perform poorly in adversarial IoT environments (e.g., wireless jamming). Specifically, DNN models are shown to be vulnerable to adversarial examples generated by introducing a subtle perturbation. Thus, the primary aim of the proposed research is to develop robust solutions for wireless localization in adversarial IoT environments, which fills in the gap between wireless localization accuracy and robustness. Particularly, we consider adversarial machine learning for wireless localization in IoT environments. The successful completion of this project will significantly improve the state-of-the-art of wireless localization and enable robust IoT applications. The project's educational plan includes developing a new graduate-level course on deep learning for wireless IoT systems and enhancing various core undergraduate and graduate-level courses. Also, the project strives to broaden participation from under-represented groups in research and will continue to greatly strengthen such efforts throughout the project years.The project research agenda is composed of two closely integrated research thrusts. In Thrust I, this project will use adversarial deep learning for indoor localization in a way that leverages adversarial training in the offline stage to improve the robustness of the deep network, thus alleviating the threat of the adversarial example attacks on wireless data. This project will consider two wireless localization tasks: adversarial examples for wireless localization in black-box attacks and unsupervised learning for adversarial examples detection. In Thrust II, this project will combine deep learning and Gaussian processes for uncertain location estimation, to improve robustness for wireless localization algorithms. Specifically, this project will exploit uncertainty location estimation with deep Gaussian process against both white-box and black-box attacks. Also, this project will model and analyze the fundamental limits and robustness of wireless localization. For all the proposed tasks in the two thrusts, this project will develop mathematical models and solution algorithms. The proposed algorithms will be implemented with wireless IoT devices/platforms (e.g., Wi-Fi, RFID, and LoRa), and validated with extensive experiments in representative indoor environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着无线网络和移动的设备的激增，无线物联网（IoT）应用（例如，基于位置的服务）已经获得了相当大的关注。室内定位在无线电传播环境中面临许多挑战，包括多径效应、阴影、衰落和延迟失真。为了解决非视距（NLOS）室内环境，已经提出了使用深度神经网络（DNN）的基于指纹的无线定位方法。然而，使用DNN的仅数据驱动的方法在对抗性IoT环境中可能表现不佳（例如，无线干扰）。具体而言，DNN模型容易受到通过引入细微扰动生成的对抗性示例的影响。因此，所提出的研究的主要目的是在对抗性物联网环境中开发强大的无线定位解决方案，填补无线定位精度和鲁棒性之间的差距。特别是，我们考虑对抗机器学习在物联网环境中的无线定位。该项目的成功完成将显著提高无线定位的最新水平，并实现强大的物联网应用。该项目的教育计划包括为无线物联网系统开发一门新的研究生深度学习课程，并加强各种核心本科和研究生课程。此外，该项目还努力扩大代表性不足群体参与研究的范围，并将在整个项目期间继续大力加强这方面的努力。在Thrust I中，该项目将使用对抗性深度学习进行室内定位，利用离线阶段的对抗性训练来提高深度网络的鲁棒性，从而减轻对抗性示例攻击对无线数据的威胁。该项目将考虑两个无线定位任务：黑盒攻击中无线定位的对抗性示例和对抗性示例检测的无监督学习。在Thrust II中，该项目将结合联合收割机深度学习和高斯过程进行不确定位置估计，以提高无线定位算法的鲁棒性。具体而言，该项目将利用深度高斯过程的不确定性位置估计来对抗白盒和黑盒攻击。此外，这个项目将建模和分析无线定位的基本限制和鲁棒性。对于这两个重点中的所有拟议任务，本项目将开发数学模型和求解算法。所提出的算法将用无线IoT设备/平台（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Adversarial Human Activity Recognition Using Wi-Fi CSI

• DOI: 10.1109/ccece53047.2021.9569098
• 发表时间: 2021-09
• 期刊: 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
• 作者: Harshit Ambalkar;Xuyu Wang;S. Mao

Voice Fingerprinting for Indoor Localization with a Single Microphone Array and Deep Learning

• DOI: 10.1145/3522783.3529528
• 发表时间: 2022-05
• 期刊: Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning
• 作者: Shivenkumar Parmar;Xuyu Wang;Chao Yang;S. Mao

Adversarial Deep Learning for Indoor Localization With Channel State Information Tensors

• DOI: 10.1109/jiot.2022.3155562
• 发表时间: 2022-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xiangyu Wang;Xuyu Wang;S. Mao;Jian Zhang;Senthilkumar C. G. Periaswamy;J. Patton

RFID Tag Localization With a Sparse Tag Array

• DOI: 10.1109/jiot.2021.3137723
• 发表时间: 2022-09
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Chao Yang;Xuyu Wang;S. Mao