PhD Dissertation Defense by YU Jiongchi | Securing Cloud-Native Systems: From Vulnerability Analysis to External and Insider Threat Detection

ABOUT THE TALK

Cloud-native systems have become the backbone of modern software infrastructure, but their dynamic and heterogeneous nature introduces significant security challenges. Existing approaches remain limited by insufficient understanding of infrastructure software defects, weak robustness of attack detection under evolving system behaviors, and the lack of realistic data for modeling complex insider threats.

This dissertation bridges these gaps through a threat-driven perspective spanning infrastructure vulnerabilities, external attack detection, and insider threat analysis. It presents a large-scale empirical study of defects in cloud runtime systems, investigates the robustness of log-based attack detection under realistic system dynamics, and proposes an automated framework for generating high-fidelity insider threat data using LLM-driven multi-agent simulation. Extensive evaluations demonstrate the limitations of existing security techniques and highlight the importance of robustness, realism, and adaptability in securing cloud-native environments.

ABOUT THE SPEAKER

Jiongchi YU is a final-year Ph.D. candidate in Computer Science at Singapore Management University (SMU). His research focuses on cloud-native system security and intelligent software engineering. He has published more than 20 publications at top-tier software engineering and security venues, including first-author papers at NDSS, ICSE, FSE, and ISSTA. He has twice received the SMU Presidential Doctoral Fellowship and has been recognized on the SCIS Dean's List. He actively contributes to the research community as a program committee member and reviewer for leading conferences and journals (e.g., ASE, WWW, KDD), and received the MSR 2025 Distinguished Junior Reviewer Award. More details can be found at https://ttfish.cc.