10 citations · 28 across the 13 of their papers we have counts for
14 papers
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
Yalin E. Sagduyu, Tugba Erpek, Yi Shi
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learn…
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
Yi Shi, Yalin E. Sagduyu
Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is…
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault d…
Jamming Attacks on Federated Learning in Wireless Networks
Yi Shi, Yalin E. Sagduyu
Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training d…
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
Brian Kim, Yi Shi, Yalin E. Sagduyu +2
We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a de…
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning
Yi Shi, Yalin E. Sagduyu
An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to class…
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…
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
Nof Abuzainab, Tugba Erpek, Kemal Davaslioglu +8
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To…
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu +3
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…
IoT Network Security from the Perspective of Adversarial Deep Learning
Yalin E. Sagduyu, Yi Shi, Tugba Erpek
Machine learning finds rich applications in Internet of Things (IoT) networks such as information retrieval, traffic management, spectrum sensing, and signal authentication. While…
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…
Spectrum Data Poisoning with Adversarial Deep Learning
Yi Shi, Tugba Erpek, Yalin E. Sagduyu +1
Machine learning has been widely applied in wireless communications. However, the security aspects of machine learning in wireless applications have not been well understood yet. W…
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…