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Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning

Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning

31 December 2024
Y. Sagduyu
T. Erpek
    AAML
ArXiv (abs)PDFHTML

Papers citing "Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning"

19 / 19 papers shown
Title
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting
  in Jamming Mitigation
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
Kemal Davaslioglu
Sastry Kompella
T. Erpek
Y. Sagduyu
43
1
0
14 Oct 2024
Adversarial Attacks on LoRa Device Identification and Rogue Signal
  Detection with Deep Learning
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning
Y. Sagduyu
T. Erpek
43
3
0
27 Dec 2023
White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency
  Fingerprint Identification
White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification
Jie Ma
Junqing Zhang
Guanxiong Shen
A. Marshall
Chip Hong Chang
AAML
42
5
0
14 Aug 2023
Multi-task Learning Approach for Automatic Modulation and Wireless
  Signal Classification
Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification
Anu Jagannath
Jithin Jagannath
55
30
0
25 Jan 2021
Channel Effects on Surrogate Models of Adversarial Attacks against
  Wireless Signal Classifiers
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers
Brian Kim
Y. Sagduyu
T. Erpek
Kemal Davaslioglu
S. Ulukus
AAML
56
20
0
03 Dec 2020
Deep Learning for Wireless Communications
Deep Learning for Wireless Communications
T. Erpek
Tim O'Shea
Y. Sagduyu
Yi Shi
T. Clancy
89
138
0
12 May 2020
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless
  Signal Classifiers
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
Brian Kim
Y. Sagduyu
Kemal Davaslioglu
T. Erpek
S. Ulukus
AAML
77
118
0
11 May 2020
When Wireless Security Meets Machine Learning: Motivation, Challenges,
  and Research Directions
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Y. Sagduyu
Yi Shi
T. Erpek
William C. Headley
Bryse Flowers
G. Stantchev
Zhuo Lu
AAML
57
39
0
24 Jan 2020
Deep Learning for RF Signal Classification in Unknown and Dynamic
  Spectrum Environments
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Yi Shi
Kemal Davaslioglu
Y. Sagduyu
William C. Headley
Michael Fowler
Gilbert Green
41
93
0
25 Sep 2019
Generative Adversarial Network for Wireless Signal Spoofing
Generative Adversarial Network for Wireless Signal Spoofing
Yi Shi
Kemal Davaslioglu
Y. Sagduyu
GANAAML
53
78
0
03 May 2019
Certified Adversarial Robustness via Randomized Smoothing
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M. Cohen
Elan Rosenfeld
J. Zico Kolter
AAML
169
2,052
0
08 Feb 2019
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Meysam Sadeghi
Erik G. Larsson
AAML
48
260
0
23 Aug 2018
A Survey of Machine and Deep Learning Methods for Internet of Things
  (IoT) Security
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
M. Al-garadi
Amr M. Mohamed
A. Al-Ali
Xiaojiang Du
Mohsen Guizani
73
821
0
29 Jul 2018
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
319
12,138
0
19 Jun 2017
Deep Architectures for Modulation Recognition
Deep Architectures for Modulation Recognition
Nathan E. West
Tim O'Shea
59
408
0
27 Mar 2017
Transferability in Machine Learning: from Phenomena to Black-Box Attacks
  using Adversarial Samples
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
SILMAAML
116
1,742
0
24 May 2016
Distillation as a Defense to Adversarial Perturbations against Deep
  Neural Networks
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot
Patrick McDaniel
Xi Wu
S. Jha
A. Swami
AAML
118
3,077
0
14 Nov 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,364
0
22 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,129
0
20 Dec 2014
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