10 citations · 28 across the 13 of their papers we have counts for
6 papers · 1 filter
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…