Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
Threat of adversarial attacks on DL-based IoT device identification
Z Bao, Y Lin, S Zhang, Z Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
With the rapid development of the information technology, the number of devices in the
Internet of Things (IoT) is increasing explosively, which makes device identification a great …
Internet of Things (IoT) is increasing explosively, which makes device identification a great …
When wireless security meets machine learning: Motivation, challenges, and research directions
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping
due to the shared and broadcast nature of wireless medium. To support both attack and …
due to the shared and broadcast nature of wireless medium. To support both attack and …
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 …
The Threat of Adversarial Attacks on Machine Learning in Network Security--A Survey
Machine learning models have made many decision support systems to be faster, more
accurate, and more efficient. However, applications of machine learning in network security …
accurate, and more efficient. However, applications of machine learning in network security …
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 …
Communication without interception: Defense against modulation detection
We consider a communication scenario, in which an intruder tries to determine the
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
The best defense is a good offense: Adversarial attacks to avoid modulation detection
We consider a communication scenario, in which an intruder tries to determine the
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
Membership inference attack and defense for wireless signal classifiers with deep learning
Y Shi, YE Sagduyu - IEEE Transactions on Mobile Computing, 2022 - ieeexplore.ieee.org
An over-the-air membership inference attack (MIA) is presented to leak private information
from a wireless signal classifier. Machine learning (ML) provides powerful means to classify …
from a wireless signal classifier. Machine learning (ML) provides powerful means to classify …
Adversarial attacks against deep learning based power control in wireless communications
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 …
base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a …