Wireless networks are a key component of the telecommunications infrastructure in our
society, and wireless services become increasingly important as the applications of wireless …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

An adversarial machine learning approach is introduced to launch jamming attacks on
wireless communications and a defense strategy is presented. A cognitive transmitter uses a …

Existing communication systems exhibit inherent limitations in translating theory to practice
when handling the complexity of optimization for emerging wireless applications with high …

As networks become ubiquitous in people's lives, users depend on networks a lot for
sufficient communication and convenient information access. However, networks suffer from …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

The spoofing attack is critical to bypass physical-layer signal authentication. This paper
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …

This article investigates the anti-jamming communications problem in wireless networks
from a Stackelberg game perspective. By exploring and analyzing the inherent …

An adversarial deep learning approach is presented to launch over-the-air spectrum
poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to …

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