Yalin E. Sagduyu

research

∙ 01/12/2023

Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks

Decentralized federated learning (DFL) is an effective approach to train...

0 Yi Shi, et al. ∙

research

∙ 01/11/2023

Age of Information in Deep Learning-Driven Task-Oriented Communications

This paper studies the notion of age in task-oriented communications tha...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/27/2022

Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing

Spectrum coexistence is essential for next generation (NextG) systems to...

0 Yi Shi, et al. ∙

research

∙ 12/22/2022

Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning

This paper presents a game-theoretic framework to study the interactions...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/21/2022

Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks

This paper highlights vulnerabilities of deep learning-driven semantic c...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/21/2022

Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks

This paper presents a game theoretic framework for participation and fre...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/20/2022

Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks

Semantic communications seeks to transfer information from a source whil...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/19/2022

Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects

Communications systems to date are primarily designed with the goal of r...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 11/02/2022

Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems

This paper presents a deep reinforcement learning (DRL) solution for pow...

0 Ziad El Jamous, et al. ∙

research

∙ 07/07/2022

Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning

Deep learning (DL) finds rich applications in the wireless domain to imp...

0 Kemal Davaslioglu, et al. ∙

research

∙ 04/06/2022

Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks

NextG networks are intended to provide the flexibility of sharing the sp...

0 Yi Shi, et al. ∙

research

∙ 03/09/2022

Machine Learning in NextG Networks via Generative Adversarial Networks

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorit...

0 Ender Ayanoglu, et al. ∙

research

∙ 01/13/2022

Jamming Attacks on Federated Learning in Wireless Networks

Federated learning (FL) offers a decentralized learning environment so t...

0 Yi Shi, et al. ∙

research

∙ 12/29/2021

End-to-End Autoencoder Communications with Optimized Interference Suppression

An end-to-end communications system based on Orthogonal Frequency Divisi...

0 Kemal Davaslioglu, et al. ∙

research

∙ 12/21/2021

Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

By moving from massive antennas to antenna surfaces for software-defined...

0 Brian Kim, et al. ∙

research

∙ 12/08/2021

Autoencoder-based Communications with Reconfigurable Intelligent Surfaces

This paper presents a novel approach for the joint design of a reconfigu...

0 Tugba Erpek, et al. ∙

research

∙ 09/16/2021

Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

We consider adversarial machine learning based attacks on power allocati...

0 Brian Kim, et al. ∙

research

∙ 07/22/2021

Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning

An over-the-air membership inference attack (MIA) is presented to leak p...

0 Yi Shi, et al. ∙

research

∙ 04/09/2021

Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) are expected to be an integral part of w...

0 Xueyuan Wang, et al. ∙

research

∙ 03/25/2021

Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond

Deep learning provides powerful means to learn from spectrum data and so...

0 Brian Kim, et al. ∙

research

∙ 02/22/2021

Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction

We consider the problem of proactive handoff and beam selection in Terah...

0 Nof Abuzainab, et al. ∙

research

∙ 01/21/2021

Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing

Network slicing manages network resources as virtual resource blocks (RB...

0 Yi Shi, et al. ∙

research

∙ 01/14/2021

How to Attack and Defend 5G Radio Access Network Slicing with Reinforcement Learning

Reinforcement learning (RL) for network slicing is considered in the 5G ...

0 Yi Shi, et al. ∙

research

∙ 01/07/2021

Adversarial Machine Learning for 5G Communications Security

Machine learning provides automated means to capture complex dynamics of...

41 Yalin E. Sagduyu, et al. ∙

research

∙ 01/06/2021

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

We present DeepIA, a deep neural network (DNN) framework for enabling fa...

22 Tarun S. Cousik, et al. ∙

research

∙ 12/03/2020

Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers

We consider a wireless communication system that consists of a backgroun...

4 Brian Kim, et al. ∙

research

∙ 09/14/2020

Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

The paper presents a reinforcement learning solution to dynamic resource...

0 Yi Shi, et al. ∙

research

∙ 07/31/2020

Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers

We consider a wireless communication system, where a transmitter sends s...

0 Brian Kim, et al. ∙

research

∙ 07/16/2020

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

The spoofing attack is critical to bypass physical-layer signal authenti...

0 Yi Shi, et al. ∙

research

∙ 06/25/2020

Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers

This paper presents how to leak private information from a wireless sign...

0 Yi Shi, et al. ∙

research

∙ 06/25/2020

Adversarial Machine Learning based Partial-model Attack in IoT

As Internet of Things (IoT) has emerged as the next logical stage of the...

0 Zhengping Luo, et al. ∙

research

∙ 06/22/2020

Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks

This paper presents DeepIA, a deep learning solution for faster and more...

0 Tarun S. Cousik, et al. ∙

research

∙ 05/15/2020

How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy

We consider the problem of hiding wireless communications from an eavesd...

0 Brian Kim, et al. ∙

research

∙ 05/12/2020

Deep Learning for Wireless Communications

Existing communication systems exhibit inherent limitations in translati...

0 Tugba Erpek, et al. ∙

research

∙ 05/11/2020

Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers

This paper presents channel-aware adversarial attacks against deep learn...

0 Brian Kim, et al. ∙

research

∙ 02/05/2020

Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels

We consider a wireless communication system that consists of a transmitt...

0 Brian Kim, et al. ∙

research

∙ 01/24/2020

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

Wireless systems are vulnerable to various attacks such as jamming and e...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 12/20/2019

Coherent Communications in Self-Organizing Networks with Distributed Beamforming

Coherent communications aim to support higher data rates and extended co...

0 Yi Shi, et al. ∙

research

∙ 12/11/2019

Cross-layer Design and SDR Implementation of DSA, Backpressure Routing and Network Coding

A cross-layer cognitive radio system is designed to support unicast and ...

0 Sohraab Soltani, et al. ∙

research

∙ 11/01/2019

Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

An adversarial deep learning approach is presented to launch over-the-ai...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 10/29/2019

DeepWiFi: Cognitive WiFi with Deep Learning

We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE ...

0 Kemal Davaslioglu, et al. ∙

research

∙ 10/23/2019

Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning

We present a Trojan (backdoor or trapdoor) attack that targets deep lear...

0 Kemal Davaslioglu, et al. ∙

research

∙ 10/13/2019

QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

The problem of quality of service (QoS) and jamming-aware communications...

0 Nof Abuzainab, et al. ∙

research

∙ 10/13/2019

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

We designed and implemented a deep learning based RF signal classifier o...

0 Sohraab Soltani, et al. ∙

research

∙ 09/25/2019

Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

Dynamic spectrum access (DSA) benefits from detection and classification...

0 Yi Shi, et al. ∙

research

∙ 05/31/2019

IoT Network Security from the Perspective of Adversarial Deep Learning

Machine learning finds rich applications in Internet of Things (IoT) net...

0 Yalin E. Sagduyu, et al. ∙

research

∙ 05/04/2019

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

Defense strategies have been well studied to combat Byzantine attacks th...

0 Zhengping Luo, et al. ∙

research

∙ 05/03/2019

Generative Adversarial Network for Wireless Signal Spoofing

The paper presents a novel approach of spoofing wireless signals by usin...

0 Yi Shi, et al. ∙

research

∙ 01/26/2019

Spectrum Data Poisoning with Adversarial Deep Learning

Machine learning has been widely applied in wireless communications. How...

0 Yi Shi, et al. ∙

research

∙ 01/25/2019

Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data

As online systems based on machine learning are offered to public or pai...

0 Yi Shi, et al. ∙

Yalin E. Sagduyu

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