58 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Virginia TechUS9 papers
- University of Maryland, College ParkUS4 papers
- Arizona State UniversityUS2 papers
- University of MiamiUS2 papers
- University of South FloridaUS2 papers
- Centre National de la Recherche ScientifiqueFR1 paper
- École & Observatoire des Sciences de la TerreFR1 paper
- Georgia Institute of TechnologyUS1 paper
- ObjectVideo (United States)US1 paper
- Old Dominion UniversityUS1 paper
- ShanghaiTech UniversityCN1 paper
- Syracuse UniversityUS1 paper
30 papers
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…
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning
Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek +1
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs, w…
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond
Brian Kim, Yalin E. Sagduyu, Tugba Erpek +1
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.…
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing
Yi Shi, Yalin E. Sagduyu
Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE)…
Adversarial Machine Learning for 5G Communications Security
Yalin E. Sagduyu, Tugba Erpek, Yi Shi
Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. A…
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks an…
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
Brian Kim, Yalin E. Sagduyu, Tugba Erpek +2
We consider a wireless communication system, where a transmitter sends signals to a receiver with different modulation types while the receiver classifies the modulation types of t…
Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing
Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu
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 tha…
Adversarial Machine Learning based Partial-model Attack in IoT
Zhengping Luo, Shangqing Zhao, Zhuo Lu +2
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting div…
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers
Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu
This paper presents how to leak private information from a wireless signal classifier by launching an over-the-air membership inference attack (MIA). As machine learning (ML) algor…
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
Tarun S. Cousik, Vijay K. Shah, Jeffrey H. Reed +2
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By u…
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu +2
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is presen…
Deep Learning for Wireless Communications
Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu +2
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with h…
Coherent Communications in Self-Organizing Networks with Distributed Beamforming
Yi Shi, Yalin E. Sagduyu
Coherent communications aim to support higher data rates and extended connectivity at lower power consumption compared with traditional point-to-point transmissions. The typical se…
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
Yalin E. Sagduyu, Yi Shi, Tugba Erpek
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 predict…
DeepWiFi: Cognitive WiFi with Deep Learning
Kemal Davaslioglu, Sohraab Soltani, Tugba Erpek +1
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiF…
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning
Kemal Davaslioglu, Yalin E. Sagduyu
We present a Trojan (backdoor or trapdoor) attack that targets deep learning applications in wireless communications. A deep learning classifier is considered to classify wireless…
Generative Adversarial Network for Wireless Signal Spoofing
Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu
The paper presents a novel approach of spoofing wireless signals by using a general adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably dis…
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Zhengping Luo, Shangqing Zhao, Zhuo Lu +2
Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a f…
Interference Regime Enforcing Rate Maximization for Non-Orthogonal Multiple Access (NOMA)
Tugba Erpek, Sennur Ulukus, Yalin E. Sagduyu
An interference regime enforcing rate maximization scheme is proposed to maximize the achievable ergodic sum-rate of the parallel Gaussian interference channels by enforcing very s…
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…
Deep Learning for Launching and Mitigating Wireless Jamming Attacks
Tugba Erpek, Yalin E. Sagduyu, Yi Shi
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 pre-…
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
Kevan Yuen, Mohan M. Trivedi
In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the moni…
Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis
Sourabh Vora, Akshay Rangesh, Mohan M. Trivedi
Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful…