Defending Wireless Receivers Against Adversarial Attacks on Modulation Classifiers
Author:
Affiliation:
1. Electrical Engineering Department, École de Technologie Supérieure, University of Quebec, Montreal, QC, Canada
2. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
Funder
Tier 2 Canada Research Chair on the Next Generations of Wireless IoT Networks
the Fonds de recherche du Québec B2X Scholarship
Microsoft Research Ph.D. Fellowship
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10288305/10138665.pdf?arnumber=10138665
Reference27 articles.
1. Adversarial Attacks on Deep-Learning Based Radio Signal Classification
2. Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
3. Adversarial Examples: Attacks and Defenses for Deep Learning
4. The threat of adversarial attacks on machine learning in network security—A survey;ibitoye;arXiv 1911 02621,2019
5. Threats of Adversarial Attacks in DNN-Based Modulation Recognition
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