CAREER: Data-Driven Wireless Networking Designs for Efficiency and Security

职业：数据驱动的无线网络设计以提高效率和安全性

• 批准号: 2044516
• 负责人: Zhuo Lu
• 金额: $ 50万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044516&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Wireless; Networking

Today's wireless networks are being re-shaped by ubiquitous wireless connectivity and emerging network architectures. To meet the increasingly growing demand from the users for more efficient, reliable, and secure wireless network services, the project aims at exploring the new dimension of creating data-driven approaches towards improving the wireless network performance and security. One key difference between the proposed data-driven approaches and traditional network designs is that the online data due to network behaviors and activities will be collected, processed, and used in systematic ways towards improving wireless network efficiency, reliability, and security. The research will create, design, and optimize efficient methodologies to categorize, process, classify, use, and validate wireless network data for network performance and security.The project will focus on efficiently harnessing and leveraging the online data observed in wireless networking to improve network efficiency and security. Specifically, the research team in this project aims at i) creating data-driven approaches based on network link data to improve the throughput performance of the wireless links, ii) effectively managing and mitigating wireless collisions and interference by collecting and processing network control and data packets, iii) understanding how new security threats and attack strategies can emerge and behave based on observed network data in the wireless channel as well as designing effective countermeasure against such threats and attacks, iv) conducting extensive simulations and experimental evaluations to validate the data-driven approaches towards wireless network efficiency, reliability and security. In addition, appropriate elements in the research from the project will be integrated into educational materials. The research results will also be widely disseminated to the public.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.

当今的无线网络被无处不在的无线连接和新兴网络体系结构重新形状。为了满足用户对更高效，可靠和安全的无线网络服务的日益增长的需求，该项目旨在探索创建数据驱动方法的新维度，以改善无线网络性能和安全性。提议的数据驱动方法和传统网络设计之间的一个关键区别是，由于网络行为和活动而引起的在线数据将以系统的方式收集，处理和使用，以提高无线网络效率，可靠性和安全性。该研究将创建，设计和优化有效的方法，以对无线网络数据进行分类，处理，分类，使用和验证网络性能和安全性。该项目将集中于有效利用和利用无线网络中观察到的在线数据以提高网络效率和安全性。具体而言，该项目的研究团队的目标是i）基于网络链接数据创建数据驱动的方法，以改善无线链接的吞吐量性能，ii）有效管理和减轻无线碰撞和干扰，通过收集和处理网络控制和数据包来收集和处理网络控制和数据包，III） iv）进行广泛的模拟和实验评估，以验证数据驱动的无线网络效率，可靠性和安全性的方法。此外，该项目研究中的适当要素将集成到教育材料中。该研究结果也将被广泛传播给公众。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知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

MUSTER: Subverting User Selection in MU-MIMO Networks

• DOI: 10.1109/infocom48880.2022.9796815
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Tao Hou;Shen Bi;Tao Wang;Zhuo Lu;Yao-Hong Liu;S. Misra;Y. Sagduyu

How Can the Adversary Effectively Identify Cellular IoT Devices Using LSTM Networks?

攻击者如何使用 LSTM 网络有效识别蜂窝物联网设备？

• 发表时间: 2023
• 期刊: ACM Workshop on Wireless Security and Machine Learning
• 作者: Luo, Zhengping;Pitera, Will;Zhao, Shangqing;Lu, Zhuo;Sagduyu, Yalin
• 通讯作者: Sagduyu, Yalin

Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security

• DOI: 10.1109/icccn58024.2023.10230189
• 发表时间: 2023-06
• 期刊: 2023 32nd International Conference on Computer Communications and Networks (ICCCN)
• 作者: Jiahao Xue;Zhe Qu;Shangqing Zhao;Yao-Hong Liu;Zhuo Lu

Undermining Deep Learning Based Channel Estimation via Adversarial Wireless Signal Fabrication

• DOI: 10.1145/3522783.3529525
• 发表时间: 2022-05
• 期刊: Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning
• 作者: Tao Hou;Tao Wang;Zhuo Lu;Yao-Hong Liu;Y. Sagduyu