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

Research Interests

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

R. Ford, H. Chen, P. Madadi, M. Kulkarni, X. Ma, D. Burghal, G. Chen, Y. Hu, C. Tarver, P. Skrimponis, V. Loseu, Y. Zhang, Y. Xin, Y. Li, J. Zhang, S. Khunteta, Y. Reddy, A. Chavva, M. Kothiwale, and D. Villa. "Sim2Field: End-to-End Development of AI RANs for 6G", Proceedings of ACM Workshop on Open and AI RAN (2025),

J. Groen, S. Valerio, I. Karim, D. Villa, Y. Zhang, L. Bonati, M. Polese, S. D'Oro, T. Melodia, E. Bertino, F. Cuomo, and K. Chowdhury. "TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environments", ACM Transactions on Privacy and Security (2024), DOI:10.48550/arXiv.2412.13049

5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (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 paper, 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.

Link
D. Uvaydov, M. Zhang, C. Robinson, S. D'Oro, T. Melodia, and F. Restuccia. "Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal Stitching", {IEEE INFOCOM 2024 - IEEE Conference on Computer Communications (2024), DOI:10.1109/INFOCOM52122.2024.10621332
H. Cheng, P. Johari, M. Arfaoui, F. Periard, P. Pietraski, G. Zhang, and T. Melodia. "Real-Time AI-Enabled CSI Feedback Experimentation with Open RAN", 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS) (2024), DOI:10.23919/WONS60642.2024.10449603
M. Polese, R. Jana, V. Kounev, K. Zhang, S. Deb, and M. Zorzi. "Exploiting spatial correlation for improved user prediction in 5G cellular networks", Proc. of the Information Theory and Applications Workshop (2019),

M. Zhang, M. Polese, M. Mezzavilla, J. Zhu, S. Rangan, S. Panwar, and M. Zorzi. "Will TCP Work in mmWave 5G Cellular Networks?", IEEE Communications Magazine (2019), DOI:10.1109/MCOM.2018.1701370
L. Zhang, F. Restuccia, T. Melodia, and S. Pudleswki. "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),

F. Restuccia, S. D'Oro, L. Zhang, and T. Melodia. "The Role of Machine Learning and Radio Reconfigurability in the Quest for Wireless Security", Proactive and Dynamic Network Defense (2019), DOI:10.1007/978-3-030-10597-6_8

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.

Link
L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski. "Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach", IEEE Transactions on Mobile Computing (2018),

M. Mezzavilla, M. Zhang, M. Polese, R. Ford, S. Dutta, S. Rangan, and M. Zorzi. "End-to-End Simulation of 5G mmWave Networks", IEEE Communications Surveys Tutorials (2018), DOI:10.1109/COMST.2018.2828880
M. Polese, R. Jana, V. Kounev, K. Zhang, S. Deb, and M. Zorzi. "Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks", arXiv preprint arXiv:1808.07647 (2018), DOI:10.48550/arXiv.1808.07647
L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski. "Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications", Proc. of IEEE Conf. on Communications and Network Security (2017),

M. Polese, M. Mezzavilla, M. Zhang, J. Zhu, S. Rangan, S. Panwar, and M. Zorzi. "milliProxy: A TCP proxy architecture for 5G mmWave cellular systems", Proc. of the 51st Asilomar Conference on Signals, Systems, and Computers (2017), DOI:10.1109/ACSSC.2017.8335489
M. Zhang, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi. "ns-3 Implementation of the 3GPP MIMO Channel Model for Frequency Spectrum Above 6 GHz", Proceedings of the Workshop on ns-3 (2017), DOI:10.1145/3067665.3067678
L. Zhang, S. Amin, and C. Westphal. "Demo: VR Video Conferencing over Named Data Networks", Proc. of ACM Conference on Information-Centric Networking (ICN) (2017), DOI:10.1145/3125719.3132092
L. Zhang, S. Amin, and C. Westphal. "VR Video Conferencing over Named Data Networks", Proc. of the Workshop on Virtual Reality and Augmented Reality Network (2017), DOI:10.1145/3097895.3097897
L. Zhang, Z. Guan, and T. Melodia. "United Against the Enemy: Anti-Jamming Based on Cross-Layer Cooperation in Wireless Networks", IEEE Transactions on Wireless Communications (2016),

L. Zhang and T. Melodia. "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), DOI:10.1109/CNS.2015.7346847
L. Zhang, Z. Guan, and T. Melodia. "Cooperative Anti-jamming for Infrastructure-less Wireless Networks with Stochastic Relaying", Proc. of IEEE Conference on Computer Communications (INFOCOM) (2014),

Y. Zhang, Q. Zhang, and T. Melodia. "A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks", IEEE Communications Letters (2010), DOI:10.1109/LCOMM.2010.06.091954
A. Zhang, Q. Zhang, and T. Melodia. "A Frequency-domain Entropy-Based Spectrum Detector for Cognitive Radio Networks", IEEE Communications Letters (1970),