Zhen Zeng

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Dr. Zeng’s general research interest lies in effectively securing distributed systems, networks, and cloud environments. She is interested in AI for cybersecurity, threat intelligence, and advancing cybersecurity education for the workforce and professionals. She has served on the federal grant review panel, the technical program committee, and the organization committee for conferences and workshops on cloud computing and cybersecurity. Before joining UWM in 2023, she gained valuable industry experience in the high-tech sector, focusing on power management and cloud native technologies. Her recent research project focuses on enhancing cybersecurity for advanced manufacturing systems.

Education

• PhD, Computer Science, Arizona State University, 2022
• MS, Technology Leadership and Innovation, Purdue University, 2014
• BS, Telecommunication Engineering, Xidian University, 2005

Research Interests

• Cybersecurity
• Threat Intelligence
• Artificial Intelligence
• Distributed System
• Cloud Native
• Cybersecurity Education

Community Involvement

• Panelist, NSF Proposal Review Panels, 2024

• Technical Program Committee, CCS 2024 workshop on Adaptive and Autonomous Cyber Defense, NDSS 2024 workshop on AI System with Confidential Computing , CCS 2023 workshop on Moving Target Defense

• Social Media Chair, IEEE International Conference on Cloud Engineering 2024

• Faculty Mentor, Wisconsin Louis Stokes Alliance for Minority Participation (WiscAMP) STEM-Inspire Program since 2023

Selected Honors & Awards

• "The Teacher Excellence Award" at UWM, 2024 Spring

Selected Publications

• Zhang, Z., Elsharef, I., Zeng, Z., 2024. Unveiling Neural Network Data Free Backdoor Threats in Industrial Control Systems, In Proceedings of the 2024 ACM Computer and Communications Security Workshop on Re-design Industrial Control Systems with Security, Oct. 2024.
• Zeng, Z., Chung, C., Xie, L., 2024. The Development of A Large-Scale Cloud Emulator, In 2024 IEEE International Conference on Cloud Engineering (IC2E), Sept. 2024.
• Elsharef, I., Zeng, Z., Gu, Z, 2024. Facilitating Threat Modeling by Leveraging Large Language Models, The Network and Distributed System Security Symposium workshop on AI System with Confidential Computing 2024.
• Zeng, Z., Huang, D., Xue, G., Deng, Y., Vadnere, N. and Xie, L., 2023. ILLATION: Improving Vulnerability Risk Prioritization By Learning From Network. IEEE Transactions on Dependable and Secure Computing (TDSC).
• Zeng, Z., Chung, C. J., & Xie, L. 2022, April. Security Challenges for Modern Data Centers with IoT: A Preliminary Study. In Companion Proceedings of the Web Conference 2022.
• Zeng, Z., Yang, Z., Huang, D., & Chung, C. J. 2021. LICALITY—Likelihood and Criticality: Vulnerability Risk Prioritization Through Logical Reasoning and Deep Learning. IEEE Transactions on Network and Service Management (TNSM).
• Deng, Y., Zeng, Z., & Huang, D. 2021, June. Neocyberkg: Enhancing cybersecurity laboratories with a machine learning-enabled knowledge graph. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE).
• Zeng, Z., Deng, Y., Hsiao, I., Huang, D., & Chung, C. J. 2018, October. Improving student learning performance in a virtual hands-on lab system in cybersecurity education. In 2018 IEEE Frontiers in Education Conference (FIE).
• Deng, Y., Lu, D., Chung, C. J., Huang, D., & Zeng, Z. 2018, October. Personalized learning in a virtual hands-on lab platform for computer science education. In 2018 IEEE Frontiers in Education Conference (FIE).
• Zeng, Z., Deng, Y., Hsiao, S., Huang, D., & Chung, C. J. 2018, February. Conceptualizing Student Engagement in Virtual Hands-on Lab: Preliminary Findings from a Computer Network Security Course. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE).