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Found 11 papers, 2 papers with code
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
In this work, we first reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.
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.
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment.
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model.
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network.
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks
In this letter, we propose a novel method to detect adversarial inputs, by augmenting the main classification network with multiple binary detectors (observer networks) which take inputs from the hidden layers of the original network (convolutional kernel outputs) and classify the input as clean or adversarial.
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both.
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
The reliance on deep learning algorithms has grown significantly in recent years.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data.
