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Found 9 papers, 0 papers with code
TRACTOR: Traffic Analysis and Classification Tool for Open RAN
5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity.
Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces
Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers
The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle.
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power.
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not.
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
There is a transmitter that transmits signals with different modulation types.
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels
In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors.
