By injecting adversarial examples into training data, adversarial training is promising for
improving the robustness of deep learning models. However, most existing adversarial …

Machine learning finds rich applications in Internet of Things (IoT) networks such as
information retrieval, traffic management, spectrum sensing, and signal authentication. While …

Recent advances in wireless technologies have led to several autonomous deployments of
such networks. As nodes across distributed networks must co-exist, it is important that all …

Recent research demonstrated that the superficially well-trained machine learning (ML)
models are highly vulnerable to adversarial examples. As ML techniques are becoming a …

Machine learning provides automated means to capture complex dynamics of wireless
spectrum and support better understanding of spectrum resources and their efficient …

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

As the applications of deep reinforcement learning (DRL) in wireless communication grow,
sensitivity of DRL based wireless communication strategies against adversarial attacks has …

Recent deep neural networks based techniques, especially those equipped with the ability
of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown …

Concurrent advancements in machine learning (ML) and Internet of Things have allowed
several interesting interdisciplinary applications, such as classification tasks based on data …

As the will to deploy neural network models on embedded systems grows, and considering
the related memory footprint and energy consumption requirements, finding lighter solutions …