A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
[article]
2021
arXiv
pre-print
Preserved Fulltext
In this work, we propose an input-agnostic, undetectable, and robust adversarial attack against DNN-based wireless communication systems in both white-box and black-box scenarios. ...
We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. ...
CONCLUSIONS In this paper, we propose an adversarial attack using a perturbation generator model against DNN-based wireless communication systems. ...
arXiv:2102.00918v1
fatcat:f67ecbtxavd4va7yhb7k3tkaha
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. ...
We analyze the deep neural network (DNN) models performance against these attacks, where the adversarial perturbations are crafted using both the white-box and black-box attacks. ...
To the best of our knowledge, this is the first work discussing adversarial training for creating robust DNN-based model for wireless systems. ...
arXiv:2206.06592v1
fatcat:o7tvs2f6cfawzfa577xvauux5e
Adversarial Machine Learning in Wireless Communications using RF Data: A Review
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. ...
Various methods of generating adversarial examples and attack mechanisms are also described. ...
are robust to the effect of adversarial attacks in wireless communication systems. ...
arXiv:2012.14392v2
fatcat:4d3x2scwjvh33drc745mmc4gvy
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
2019
IEEE Communications Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks. ...
We also show that classical coding schemes are more robust than autoencoders against both adversarial and jamming attacks. The codes are available at [1] . ...
These findings suggest that defense mechanisms against adversarial attacks and further research on the security and robustness of deep-learning based wireless systems is a necessity. ...
doi:10.1109/lcomm.2019.2901469
fatcat:msjuyekvszeejdab4siqnoff2q
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks. ...
We also show that classical coding schemes are more robust than autoencoders against both adversarial and jamming attacks. The codes are available at [1]. ...
These findings suggest that defense mechanisms against adversarial attacks and further research on the security and robustness of deep-learning based wireless systems is a necessity. ...
arXiv:1902.08391v1
fatcat:keiikr3xongatcpgh4afxprrsy
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This paper presents channel-aware adversarial attacks against deep learning-based wireless signal classifiers. There is a transmitter that transmits signals with different modulation types. ...
Finally, a certified defense based on randomized smoothing that augments training data with noise is introduced to make the modulation classifier robust to adversarial perturbations. ...
Jana, “Certified attacks against deep learning based power control in wireless commu-
robustness to adversarial examples with differential privacy,” in IEEE nications,” in IEEE Global ...
arXiv:2005.05321v3
fatcat:bl5bgamxcrbrzm2thyr6fp56fu
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a deep neural network (DNN ...
We show that the adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications. ...
Moreover, since the wireless medium is shared and open to adversaries such as jammers, the adversarial attack poses a practical threat to the DNNs used in wireless communications. ...
arXiv:2109.08139v2
fatcat:mfdy3vqzebefnosfkgs4qhp7qa
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. ...
However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. ...
The current adversarial attacks on the DNN-based AMC systems adopt the white-box attack. ...
arXiv:2205.15743v1
fatcat:uv5mebrttrcm5btb6twt7b23fu
WiAdv
2022
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To fill this gap, we aim to study adversarial attacks to DNN-powered WiFi-based gesture recognition to encourage proper countermeasures. ...
the wireless medium and the inherent defects (e.g., adversarial attacks) of classifiers. ...
Another defense method is the beamforming-based interference suppresses approach developed in wireless communication systems. ...
doi:10.1145/3534618
fatcat:zxydnz7cizhzpow5kvi2t6zcmu
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally ...
., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics ...
Towards Robust ML-Driven 5G and Beyond Networks Robustness against adversarial ML attacks is a very challenging problem. ...
arXiv:2009.02473v1
fatcat:ihhhcrmzb5b6bfso2wrken7owq
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Experimental results show that the proposed methods effectively defend the DNN models against adversarial attacks in next-generation wireless networks. ...
This study also offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence (AI)-based models used in the millimeter-wave ...
*BS: Base Station, AP: Access Point 4 System Overview
Complex Numbers and Wireless Communication The number system we use in our daily life is based on a real number system. ...
arXiv:2202.08185v1
fatcat:dnhf6qxajbedlon6juph73czme
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Deep learning (DL) architectures have been successfully used in many applications including wireless systems. However, they have been shown to be susceptible to adversarial attacks. ...
We analyze DL-based models for a regression problem in the context of downlink power allocation in massive multiple-input-multiple-output systems and propose universal adversarial perturbation (UAP)-crafting ...
Given this security issue of DNNs, the study of adversarial attacks for wireless systems has received considerable attention [1] , [2] , [5] . ...
arXiv:2110.04731v1
fatcat:4x7dtet43jgfboyzsd6ozhbcce
A Survey on Adversarial Attack in the Age of Artificial Intelligence
2021
Wireless Communications and Mobile Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. ...
At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. ...
Existing defense methods are ineffective against this
attack, so new defense mechanisms are needed to
defend against it.
9
Wireless Communications and Mobile Computing
Table 4 : 4 Continued. ...
doi:10.1155/2021/4907754
fatcat:rm6xcf6ryrh6ngro4sl5ifprgy
SafeAMC: Adversarial training for robust modulation recognition models
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. ...
We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models. ...
increases the robustness of these models; • We design a new framework based on the specific properties of communication systems. ...
arXiv:2105.13746v1
fatcat:vki56gfamvbpxcg2r5iz6wjvme
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
in real-world wireless communication applications. ...
communications. ...
Two metrics are proposed in [174] for detecting adversarial attacks on wireless communications. ...
arXiv:2010.00432v1
fatcat:mxnvorh5wrfwzmxg4ezpbj4xve
« Previous
Showing results 1 — 15 out of 487 results