Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing
Y Shi, K Davaslioglu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The spoofing attack is critical to bypass physical-layer signal authentication. This paper
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …
Generative adversarial network for wireless signal spoofing
The paper presents a novel approach of spoofing wireless signals by using a general
adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably …
adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably …
Adversarial deep learning for over-the-air spectrum poisoning attacks
An adversarial deep learning approach is presented to launch over-the-air spectrum
poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to …
poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to …
Adversarial deep learning for cognitive radio security: Jamming attack and defense strategies
This paper presents an adversarial machine learning approach to launch jamming attacks
on wireless communications and introduces a defense strategy. In a cognitive radio network …
on wireless communications and introduces a defense strategy. In a cognitive radio network …
Channel-aware adversarial attacks against deep learning-based wireless signal classifiers
This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …
wireless signal classifiers. There is a transmitter that transmits signals with different …
On the limitations of targeted adversarial evasion attacks against deep learning enabled modulation recognition
Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
Evaluating adversarial evasion attacks in the context of wireless communications
B Flowers, RM Buehrer… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
Over-the-air adversarial attacks on deep learning based modulation classifier over wireless channels
We consider a wireless communication system that consists of a transmitter, a receiver, and
an adversary. The transmitter transmits signals with different modulation types, while the …
an adversary. The transmitter transmits signals with different modulation types, while the …
Defense for black-box attacks on anti-spoofing models by self-supervised learning
High-performance anti-spoofing models for automatic speaker verification (ASV), have been
widely used to protect ASV by identifying and filtering spoofing audio that is deliberately …
widely used to protect ASV by identifying and filtering spoofing audio that is deliberately …
Deep learning for launching and mitigating wireless jamming attacks
An adversarial machine learning approach is introduced to launch jamming attacks on
wireless communications and a defense strategy is presented. A cognitive transmitter uses a …
wireless communications and a defense strategy is presented. A cognitive transmitter uses a …