Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks-Reference-Cited by-同舟云学术

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Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Published:2022 Issue: Volume:10 Page:100267-100275
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Catak Ferhat Ozgur¹,Kuzlu Murat²^ORCID,Tang Haolin³,Catak Evren,Zhao Yanxiao³^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of Stavanger, Rogaland, Stavanger, Norway

2. Department of Engineering Technology, Old Dominion University, Norfolk, VA, USA

3. Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

Funder

Commonwealth Cyber Initiative (CCI) and Virginia Commonwealth University Presidential Research Qurest Fund

ICC is an Investment in the Advancement of Cyber Research and Development, Innovation, and Workforce Development in VIRGINIA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09889707.pdf?arnumber=9889707

Reference27 articles.

1. Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

2. Intelligent Spectrum Learning for Wireless Networks With Reconfigurable Intelligent Surfaces

3. Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

4. Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

5. Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adversarial Machine Learning in Industry: A Systematic Literature Review;Computers & Security;2024-10

2. Wireless Signal Denoising Using Conditional Generative Adversarial Networks;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2023-05-20

3. Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks;IEEE Access;2023

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