【2026-05-22】Prof. Yunpeng Zhang, University of Houston, “Hybrid Qunatum-Classical Computing for Intrusion Detection in Advanced Traffic Management System (ATMS) ”(English Talk)
Title：Hybrid Qunatum-Classical Computing for Intrusion Detection in Advanced Traffic Management System (ATMS)
Date：2026/5/22 14:20-15:30
Location：R103, CSIE
Speakers：Prof. Yunpeng Zhang, University of Houston
Host：Prof. Chung-Wei Lin
Abstract:
Ensuring the security of information transmission in advanced traffic management systems is crucial for maintaining the integrity and reliability of traffic operations. In this research, we present a novel intrusion detection system that leverages a hybrid quantum-classical approach to enhance network security within advanced traffic management systems. By combining the strengths of quantum computing and classical techniques, our system effectively detects and mitigates intrusions in real time. We address the challenges posed by noisy quantum environments and computational overhead, developing a model that optimizes accuracy while minimizing resource demands. To comprehensively assess the capabilities of our system, we conducted rigorous evaluations using three distinct datasets. This multiple-dataset approach enables a thorough evaluation of our model’s performance against both new and old attack types. Our intrusion detection system exhibits outstanding performance on the KDD99 dataset, surpassing an accuracy rate of 98.96% and an impressive 99.40% accuracy on CICIDS. In addition, our system demonstrates superior memory usage efficiency, outperforming all existing solutions in this domain. This achievement underscores our approach’s ability to maintain high accuracy while minimizing computational resource demands. These findings highlight the effectiveness of our approach in fortifying the security of advanced traffic management systems and demonstrate its potential for real-world deployment.
Short Bio:
Dr. Yunpeng Zhang, Ph.D., currently is an Associate Professor at the University of Houston, USA. Dr. Zhang’s research focuses on developing novel security and intelligence techniques to ensure cyber/physical system reliability, security, and performance in multiple industries, including transportation, energy, healthcare, commerce, government, the Internet of Things, etc. He is familiar with state-of-the-art research and technologies related to cyber and physical security, and artificial Intelligence, such as cryptography, access control, intrusion detection, blockchain, intelligent monitoring, and Qunatum computing. Dr. Zhang conducted more than 40 research and education projects supported by NSF, DoT, NIH, TxDoT, and private institutes, etc. He has worked for Boise State University (U.S.), Dakota State University (U.S.), Oak Ridge National Lab (U.S.), Imperial College London (U.K.), Queen’s University Belfast (U.K.), University of Melbourne (Australia), Northwestern Polytechnical University (China), etc. to become a leader in cybersecurity and autonomous systems. His career is highlighted by securing millions in funding, most notably as the lead PI for a $10 million USDOT grant. This funding established CYBER-CARE, a Tier 1 University Transportation Center where Dr. Zhang leads a consortium of elite six institutions to secure national transportation systems as the founding director. He advised more than 70 graduate students who have successful careers in high-impact institutes, e.g., Amazon, Meta, AMD, the University of New South Wales, etc. He has authored more than 120 peer-reviewed publications, six books, three book chapters and holds 6 patents. Dr. Zhang also led to practical solutions to real-world problems, e.g., invented more than 70 high-performance/security new algorithms/methods, developed 30 software systems. He received multiple research, teaching, faculty excellence awards based on his leadership, research, teaching and high-impact funding record. Dr. Zhang served chairs for more than 60 international conferences. He is keen on cooperation with academics and industry.