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

基本信息

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

项目摘要

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，随着硬件、配置和新接入技术的复杂性的增加，其难度只会增加。政府机构和无线提供商需要强大而灵活的工具来监控和检测物理频谱使用中的异常情况（即故障和不当行为），并大规模部署它们。该项目针对广域蜂窝网络频谱异常检测的公开挑战，提供实用的架构来监控、诊断和保护频谱使用。该项目将显着提高频谱效率，同时解决与能源效率和安全相关的多个开放挑战。该项目的技术部分将与教育和推广计划紧密结合，吸引女性和代表性不足的学生参与研究，同时提供指导平台。该项目将发展和加强与学术界、政府和行业合作伙伴在频谱测量、节能系统、安全和应用机器学习等多个领域的合作。推动该项目的核心理念是将分布式频谱测量平台与一种高效的基于深度神经网络的异常检测框架。这种背景下的深度学习带来了多种挑战，包括可扩展性、缺乏特定位置的训练数据、缺乏数据误用事件以及潜在的对抗性攻击。我们提出的工作通过结合上下文无关的训练、迁移学习、用于异常检测的半监督聚类和对抗性对策来解决这些问题。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，并使用这些测量来训练由深度神经网络模型驱动的高效、可扩展且鲁棒的异常检测模块。该检测模块将在静态和移动观测器上运行，使系统能够实时检测和诊断频谱异常。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

泄露后恢复：针对泄露的 DNN 模型的白盒对抗示例的防护

DOI：
10.1145/3548606.3560561
发表时间：
2022-05-21
期刊：
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
影响因子：
0
作者：
Shawn Shan;Wen;Emily Wenger;Haitao Zheng;Ben Y. Zhao
通讯作者：
Ben Y. Zhao

Understanding Robust Learning through the Lens of Representation Similarities

通过表征相似性的视角理解鲁棒学习

DOI：
10.48550/arxiv.2206.09868
发表时间：
2022-06-20
期刊：
ArXiv
影响因子：
0
作者：
Christian Cianfarani;A. Bhagoji;Vikash Sehwag;Ben Y. Zhao;Prateek Mittal
通讯作者：
Prateek Mittal

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

福克斯：保护隐私免受未经授权的深度学习模型的影响

DOI：
发表时间：
2020-01
期刊：
Proceedings of the 29th USENIX Security Symposium
影响因子：
0
作者：
Shan, Shawn;Wenger, Emily;Zhang, Jiayun;Li, Huiying;Zheng, Haitao;Zhao, Ben Y.
通讯作者：
Zhao, Ben Y.

Understanding the Effect of Bias in Deep Anomaly Detection

了解深度异常检测中偏差的影响

DOI：
10.24963/ijcai.2021/456
发表时间：
2021-05-16
期刊：
ArXiv
影响因子：
0
作者：
Ziyu Ye;Yuxin Chen;Haitao Zheng
通讯作者：
Haitao Zheng

Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods

检测性别刻板印象：词典与监督学习方法

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
影响因子：
0
作者：
Cryan, Jenna;Tang, Shiliang;Zhang, Xinyi;Metzger, Miriam;Zheng, Haitao;Zhao, Ben Y.
通讯作者：
Zhao, Ben Y.