Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …
there is a great need to deliberate on the future of the cities worth living. In particular, as …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
A very brief introduction to machine learning with applications to communication systems
O Simeone - IEEE Transactions on Cognitive Communications …, 2018 - ieeexplore.ieee.org
Given the unprecedented availability of data and computing resources, there is widespread
renewed interest in applying data-driven machine learning methods to problems for which …
renewed interest in applying data-driven machine learning methods to problems for which …
Deep learning for launching and mitigating wireless jamming attacks
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 …
wireless communications and a defense strategy is presented. A cognitive transmitter uses a …
Deep learning for wireless communications
Existing communication systems exhibit inherent limitations in translating theory to practice
when handling the complexity of optimization for emerging wireless applications with high …
when handling the complexity of optimization for emerging wireless applications with high …
Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute
an extensively-researched machine learning sub-field for the creation of synthetic data …
an extensively-researched machine learning sub-field for the creation of synthetic data …
Channel-aware adversarial attacks against deep learning-based wireless signal classifiers
This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …
wireless signal classifiers. There is a transmitter that transmits signals with different …
Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing
Y Shi, K Davaslioglu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …
Deep learning for RF signal classification in unknown and dynamic spectrum environments
Dynamic spectrum access (DSA) benefits from detection and classification of interference
sources including in-network users, out-network users, and jammers that may all coexist in a …
sources including in-network users, out-network users, and jammers that may all coexist in a …
IoT network security from the perspective of adversarial deep learning
Machine learning finds rich applications in Internet of Things (IoT) networks such as
information retrieval, traffic management, spectrum sensing, and signal authentication. While …
information retrieval, traffic management, spectrum sensing, and signal authentication. While …