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 …
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 …
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 …
On attacking future 5g networks with adversarial examples: Survey
M Zolotukhin, D Zhang, T Hämäläinen, P Miraghaei - Network, 2022 - mdpi.com
The introduction of 5G technology along with the exponential growth in connected devices is
expected to cause a challenge for the efficient and reliable network resource allocation …
expected to cause a challenge for the efficient and reliable network resource allocation …
Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems
The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G)
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
Covert communications via adversarial machine learning and reconfigurable intelligent surfaces
By moving from massive antennas to antenna surfaces for software-defined wireless
systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control …
systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control …
Downlink power allocation in massive MIMO via deep learning: Adversarial attacks and training
The successful emergence of deep learning (DL) in wireless system applications has raised
concerns about new security-related challenges. One such security challenge is adversarial …
concerns about new security-related challenges. One such security challenge is adversarial …
Backdoor federated learning-based mmWave beam selection
Federated learning (FL) is an emerging paradigm for distributed machine learning that uses
the data and the computational power of user devices while maintaining user privacy (eg …
the data and the computational power of user devices while maintaining user privacy (eg …
Defending adversarial attacks on deep learning-based power allocation in massive MIMO using denoising autoencoders
Recent work has advocated for the use of deep learning to perform power allocation in the
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …
Poison neural network-based mmWave beam selection and detoxification with machine unlearning
Deep neural network-based learning methods have been considered promising techniques
used in beam selection problems. However, existing research ignores the peculiar …
used in beam selection problems. However, existing research ignores the peculiar …