SpecEES: Scaling Deep Learning Models for Cellular Spectrum Monitoring

SpecEES：扩展用于蜂窝频谱监控的深度学习模型

• 批准号: 1923778
• 负责人: Haitao Zheng
• 金额: $ 75万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923778&HistoricalAwards=false
• 关键词: SpecEES; Scaling; Deep; Learning; Models

Spectrum management in wireless networks is a challenging task that will only increase in difficulty as complexity grows in hardware, configurations, and new access technology. Government agencies and wireless providers need robust and flexible tools to monitor and detect anomalies (i.e., both faults and misbehavior) in physical spectrum usage, and to deploy them at scale. This project targets the open challenge of spectrum anomaly detection for wide-area cellular networks, providing a practical architecture to monitor, diagnose, and secure spectrum usage. The project will significantly improve spectrum efficiency, while tackling multiple open challenges related to energy efficiency and security. The technical component of the project will be tightly integrated with educational and outreach programs to engage female and underrepresented students into research, while offering a mentoring platform. The project will develop and strengthen collaborations with academic, government and industry partners, across multiple areas of spectrum measurements, energy-efficient systems, security, and applied machine learning.The core concept driving this project is the integration of a distributed spectrum measurement platform with an efficient deep neural network-based anomaly detection framework. Deep learning in this context introduces multiple challenges, including scalability, lack of location-specific training data, lack of misuse events in data, and potential for adversarial attacks. Our proposed work addresses these with a combination of context-agnostic training, transfer learning, semi-supervised clustering for detection of anomalies and adversarial countermeasures. The proposed system will incorporate spectrum measurements taken by various static and mobile observers, and use these measurements to train an efficient, scalable, and robust anomaly detection module driven by deep neural network models. The detection module will run on both static and mobile observers, allowing the system to detect and diagnose spectrum anomalies in real-time.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.

无线网络中的频谱管理是一项具有挑战性的任务，它只会随着硬件，配置和新的访问技术的增长而增加难度。 政府机构和无线提供商需要强大而灵活的工具来监视和检测物理频谱使用情况的异常（即，故障和不当行为），并大规模部署它们。 该项目针对宽区域蜂窝网络的频谱异常检测的开放挑战，提供了一种实用的体系结构来监视，诊断和安全频谱使用情况。该项目将显着提高频谱效率，同时解决与能源效率和安全性相关的多重开放挑战。该项目的技术组成部分将与教育和外展计划紧密整合，以使女性和代表性不足的学生参与研究，同时提供指导平台。 该项目将在频谱测量，节能系统，安全性和应用机器学习的多个领域发展和加强与学术，政府和行业合作伙伴的合作。驱动该项目的核心概念是将分布式频谱测量平台与有效的基于基于神经网络的高神经网络的隔离障碍框架整合在一起。在这种情况下，深度学习引入了多个挑战，包括可伸缩性，缺乏特定位置的培训数据，缺乏数据中的滥用事件以及对抗性攻击的潜力。我们提出的工作通过上下文不足的培训，转移学习，半监督的聚类来解决这些工作，以检测异常和对抗性对策。所提出的系统将结合各种静态和移动观察者进行的频谱测量值，并使用这些测量值来训练由深神经网络模型驱动的有效，可扩展且健壮的异常检测模块。检测模块将在静态和移动观察者上同时运行，从而使该系统可以实时检测和诊断频谱异常。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN Models

• DOI: 10.1145/3548606.3560561
• 发表时间: 2022-05
• 期刊: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Shawn Shan;Wen-Luan Ding;Emily Wenger;Haitao Zheng;Ben Y. Zhao

Understanding Robust Learning through the Lens of Representation Similarities

• DOI: 10.48550/arxiv.2206.09868
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Christian Cianfarani;A. Bhagoji;Vikash Sehwag;Ben Y. Zhao;Prateek Mittal

Understanding the Effect of Bias in Deep Anomaly Detection

• DOI: 10.24963/ijcai.2021/456
• 发表时间: 2021-05
• 期刊: ArXiv
• 作者: Ziyu Ye;Yuxin Chen;Haitao Zheng

Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods

• DOI: 10.1145/3313831.3376488
• 发表时间: 2020-04
• 期刊: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Jenna Cryan;Shiliang Tang;Xinyi Zhang;Miriam J. Metzger;Haitao Zheng;Ben Y. Zhao

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

• 发表时间: 2020-02
• 作者: Shawn Shan;Emily Wenger;Jiayun Zhang;Huiying Li;Haitao Zheng;Ben Y. Zhao