Adversarial Machine Learning in Wireless Communications Using RF Data: A Review-Reference-Cited by-同舟云学术

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

Published:2023 Issue:1 Volume:25 Page:77-100
ISSN:1553-877X
Container-title:IEEE Communications Surveys & Tutorials
language:
Short-container-title:IEEE Commun. Surv. Tutorials

Author:

Adesina Damilola¹^ORCID,Hsieh Chung-Chu²,Sagduyu Yalin E.³^ORCID,Qian Lijun¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center), Prairie View A&M University, Prairie View, TX, USA

2. Center of Excellence in Cyber Security, Norfolk State University, Norfolk, VA, USA

3. National Security Institute, Virginia Tech, Arlington, VA, USA

Funder

U.S. National Science Foundation

U.S. Office of the Under Secretary of Defense for Research and Engineering (OUS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/9739/10051138/9887796-aam.pdf

Reference283 articles.

1. Targeted Adversarial Examples Against RF Deep Classifiers

2. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

3. Hacking the waveform: Generalized wireless adversarial deep learning;restuccia;arXiv 2005 02270,2020

4. Deep speech: Scaling up end-to-end speech recognition;hannun;arXiv 1412 5567,2014

5. Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers

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