58 citations
- Virginia TechUS9 papers
- University of Maryland, College ParkUS4 papers
- Georgia Institute of TechnologyUS1 paper
- Old Dominion UniversityUS1 paper
- Syracuse UniversityUS1 paper
- University of California, IrvineUS1 paper
- University of IowaUS1 paper
- University of MiamiUS1 paper
- University of South FloridaUS1 paper
7 papers · 1 filter
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 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…
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…