Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks-Reference-Cited by-同舟云学术

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

Published:2022 Issue: Volume:10 Page:98191-98203
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Catak Ferhat Ozgur¹,Kuzlu Murat²^ORCID,Catak Evren³,Cali Umit⁴^ORCID,Guler Ozgur⁵

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. 4034, Stavanger, Norway

4. Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway

5. eKare Inc., Fairfax, VA, USA

Funder

Commonwealth Cyber Initiative, 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/ielx7/6287639/9668973/09888103.pdf?arnumber=9888103

Reference46 articles.

1. Adversarial Examples in the Physical World

2. Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples;tuna;arXiv 2102 04150,2021

3. Adversarial attacks on medical machine learning

4. Convolutional networks and applications in vision

5. Backpropagation Applied to Handwritten Zip Code Recognition

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