68 citations · 278 across the 34 of their papers we have counts for
6 papers · 1 filter
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu +1
This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmi…
Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals
Srihari Kamesh Kompella, Kemal Davaslioglu, Yalin E. Sagduyu +1
Radio frequency (RF) communication has been an important part of civil and military communication for decades. With the increasing complexity of wireless environments and the growi…
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
Kemal Davaslioglu, Sastry Kompella, Tugba Erpek +1
Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliabl…
Machine Learning in NextG Networks via Generative Adversarial Networks
Ender Ayanoglu, Kemal Davaslioglu, Yalin E. Sagduyu
Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mi…
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu +1
As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and…
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu +1
Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or pa…