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20182020
most citedWhen Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

38 citations · 63 across the 4 of their papers we have counts for

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Showing eess.SPShow all

5 papers · 1 filter

eess.SP2024

Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture

Samuel B. Brown, Stephen Young, Adam Wagenknecht +4

Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, par…

eess.SP202024 cited

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Lauren J. Wong, William H. Clark, Bryse Flowers +3

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these techn…

eess.SP20201 cited

Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features

J. B. Persons, Lauren J. Wong, W. Chris Headley +1

To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and…

eess.SP2019

Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications

Bryse Flowers, R. Michael Buehrer, William C. Headley

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, deep…

eess.SP2018

Emitter Identification Using CNN IQ Imbalance Estimators

Lauren J. Wong, William C. Headley, Alan J. Michaels

Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an em…