Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks-Reference-Cited by-同舟云学术

Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks

Published:2023 Issue: Volume:11 Page:76629-76637
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Tang Haolin¹^ORCID,Catak Ferhat Ozgur²^ORCID,Kuzlu Murat³^ORCID,Catak Evren⁴,Zhao Yanxiao¹^ORCID

Affiliation:

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

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

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

4. Independent Researcher, Stavanger, Norway

Funder

Commonwealth Cyber Initiative (CCI), 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/ielx7/6287639/10005208/10187134.pdf?arnumber=10187134

Reference23 articles.

1. Angle of arrival based signal classification in intelligent reflecting surface-aided wireless communications;tang;Proc Int Conf Mobile Comput Appl Services,2023

2. Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

3. Long Short-Term Memory

4. Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors

5. Security and Threats of Intelligent Reflecting Surface Assisted Wireless Communications

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3. Security of AI-Driven Beam Selection for Distributed MIMO in an Adversarial Setting;IEEE Access;2024