Bryse Flowers

On the Limitations of Targeted Adversarial Evasion Attacks Against Deep Learning Enabled Modulation Recognition

Samuel Bair, Matthew DelVecchio, Bryse Flowers et al. · 2019 · 62 citations

Wireless Communications, Engineering, Machine Learning +11

When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

Yalin E. Sagduyu, Yi Shi, Tugba Erpek et al. · arXiv (Cornell University) · 2020 · 38 citations · Full text

Wireless Communications, Engineering, Machine Learning +20

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 et al. · arXiv (Cornell University) · 2020 · 24 citations · Full text

Artificial Intelligence, Wireless Communications, Deep Machine Learning +21

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Lauren J. Wong, William H. Clark, Bryse Flowers et al. · IEEE Open Journal of the Communications Society · 2021 · 24 citations · Full text

Wireless Communications, Engineering, Machine Learning +22

Communications Aware Adversarial Residual Networks for over the Air Evasion Attacks

Bryse Flowers, R. Michael Buehrer, William C. Headley · 2019 · 23 citations

Data Augmentation, Engineering, Machine Learning +15