Yangyong Zhang
B.S. in Electrical Engineering
Education
- B.S. in Electrical Engineering - State University of New York at Buffalo
- Industrial Automation (2 years) - Shanghai Institute of Technology, China
Yangyong Zhang earned his B.S. in Electrical Engineering from the University at Buffalo, State University of New York. He began his studies at the Shanghai Institute of Technology majoring in Industrial Automation, and after two years transferred to UB to complete his degree. He worked in the Wireless Networks and Embedded Systems Laboratory under Professor Tommaso Melodia, where his research focused on ultrasonic intra-body area networks.
Publications
2025
Journals & Magazines
5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity.In this article, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.
Conference Papers
Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), the wireless industry is now broadly pushing for the adoption of AI in 5G and future 6G technology. Despite this enthusiasm, AI-based wireless systems still remain largely untested in the field. Common simulation methods for generating datasets for AI model training suffer from “reality gap” and, as a result, the performance of these simulation-trained models may not carry over to practical cellular systems. Additionally, the cost and complexity of developing high-performance proof-of-concept implementations present major hurdles for evaluating AI wireless systems in the field. In this work, we introduce a methodology which aims to address the challenges of bringing AI to real networks. We discuss how detailed Digital Twin simulations may be employed for training site-specific AI Physical (PHY) layer functions. We further present a powerful testbed for AI-RAN research and demonstrate how it enables rapid prototyping, field testing and data collection. Finally, we evaluate an AI channel estimation algorithm over-the-air with a commercial UE, demonstrating that real-world throughput gains of up to 40% are achievable by incorporating AI in the physical layer.
2024
Conference Papers
“Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal Stitching.”
{IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
(2024)Conference
2019
Journals & Magazines
“Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks.”
Proc. of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
(2019)Journal
“Exploiting spatial correlation for improved user prediction in 5G cellular networks.”
Proc. of the Information Theory and Applications Workshop
(2019)Article
Book Chapters
Wireless networks require fast-acting, effective and efficient security mechanisms able to tackle unpredictable, dynamic, and stealthy attacks. In recent years, we have seen the steadfast rise of technologies based on machine learning and software-defined radios, which provide the necessary tools to address existing and future security threats without the need of direct human-in-the-loop intervention. On the other hand, these techniques have been so far used in an ad hoc fashion, without any tight interaction between the attack detection and mitigation phases. In this chapter, we propose and discuss a Learning-based Wireless Security (LeWiS) framework that provides a closed-loop approach to the problem of cross-layer wireless security. Along with discussing the LeWiS framework, we also survey recent advances in cross-layer wireless security.
2018
Journals & Magazines
“End-to-End Simulation of 5G mmWave Networks.”
IEEE Communications Surveys Tutorials
(2018)Journal
“Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach.”
IEEE Transactions on Mobile Computing
(2018)Journal
2017
Journals & Magazines
“Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications.”
Proc. of IEEE Conf. on Communications and Network Security
(2017)Journal
Conference Papers
“milliProxy: A TCP proxy architecture for 5G mmWave cellular systems.”
Proc. of the 51st Asilomar Conference on Signals, Systems, and Computers
(2017)Conference
“VR Video Conferencing over Named Data Networks.”
Proc. of the Workshop on Virtual Reality and Augmented Reality Network
(2017)Conference
“Demo: VR Video Conferencing over Named Data Networks.”
Proc. of ACM Conference on Information-Centric Networking (ICN)
(2017)Conference
2016
Journals & Magazines
“United Against the Enemy: Anti-Jamming Based on Cross-Layer Cooperation in Wireless Networks.”
IEEE Transactions on Wireless Communications
(2016)Journal
2015
Conference Papers
“Hammer and anvil: The threat of a cross-layer jamming-aided data control attack in multihop wireless networks.”
Proc. of IEEE Conference on Communications and Network Security (CNS)
(2015)Conference
2014
Conference Papers
“Cooperative Anti-jamming for Infrastructure-less Wireless Networks with Stochastic Relaying.”
Proc. of IEEE Conference on Computer Communications (INFOCOM)
(2014)Conference
2010
Journals & Magazines
“A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks.”
IEEE Communications Letters
(2010)Journal