The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

As in-the-wild data are increasingly involved in the training stage, machine learning
applications become more susceptible to data poisoning attacks. Such attacks typically lead …

Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF)
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …

Big client data and deep learning bring a new level of accuracy to wireless traffic prediction
in non-adversarial environments. However, in a malicious client environment, the training …

The past decades have witnessed significant progress towards improving the accuracy of
predictions powered by complex machine learning models. Despite much success, the lack …

Despite the proven capabilities of deep neural networks (DNNs) in identifying devices
through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep …

Dataset condensation aims to condense the original training dataset into a small synthetic
dataset for data-efficient learning. The recently proposed dataset condensation techniques …

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy
of data-driven decision-making by affecting the distribution of original data. The Quality of …

Deep learning is leading the way of numerous ongoing advances. With sufficient high-
quality training data, correct implementations, and benign training environments, deep …