Dr. Jee’s current focus primarily spans three research thrusts: (1) system provenance, (2) reversing and decompilation of High-level Dynamic Languages (HDLs), and (3) safety and security of small satellites in Low Earth Orbit (LEO). Various private organizations and public agencies, including NSF and NIST, have supported Dr. Jee’s research.
System provenance research. Dr. Jee is a leading researcher in system provenance, known for developing and deploying a comprehensive framework to collect system events across diverse, real-world networks. He has focused on automating forensic analysis tasks using system provenance data while enhancing storage and processing capacities to support these efforts. His current research explores machine learning (ML) security models built on system provenance, focusing on evasion techniques that target these models, improving model explainability, and addressing the scarcity of public datasets, alongside tackling privacy challenges associated with data sharing.
Perfect decompilation of high-level dynamic language (HDL). With software products and malware increasingly packaged and distributed in HDL bytecode, the lack of functioning decompiler support has posed a significant challenge to the community. In response, Dr. Jee’s group developed a novel approach to Python bytecode decompilation: a hybrid system combining ML and PL techniques that can adapt seamlessly to changes in the Python language over time. PyLingual, now available as a public online service, ensures a strict accuracy guarantee through perfect decompilation, enabling users to validate results and refine outputs. PyLingual was showcased at PyCon US 2024 and Black Hat USA 2024 and accepted for presentation at Oakland 2025.
Security and Safety of Small Satellites in LEO. Dr. Jee’s research focuses on small satellites’ security and safety challenges, which are becoming increasingly prevalent due to innovations in reusable rocket boosters and more powerful, resilient microelectronics (ME) technologies. He has built a strong foundation in space security research. Leveraging his expertise in the field, Dr. Jee has served as a technical panelist at the NSF SaTC workshop, presented his research at aerospace conferences, and hosted the first small satellite workshop at UT Dallas. The NSF supports Dr Jee’s research on space system security.
Selected publications (full list)
Papers are listed in chronological order.
- PyLingual: Toward Perfect Decompilation of Evolving High-Level Languages
J Wiedemeier, E Tarbet, M Zheng, S Ko, J Ouyang, SK Cha, K Jee
IEEE Symposium on Security and Privacy (Oakland), May 2025 - Evading Provenance-Based ML Detectors with Adversarial System Actions
K Mukherjee, J Wiedemeier, T Wang, J Wei, M Kim, M Kantarcioglu, K Jee
In Proceedings of Usenix Security, Aug 2023 - Reassembly is Hard: A Reflection on Challenges and Strategies
H Kim, S Kim, J Lee, K Jee, SK Cha
In Proceedings of Usenix Security, Aug 2023 - Back-Propagating System Dependency Impact for Attack Investigation
P Fang, P Gao, C Liu, E Ayday, K Jee, T Wang, Y Ye, Z Liu, X Xiao
In Proceedings of Usenix Security, Aug 2022 - SEAL: Storage-efficient Causality Analysis on Enterprise Logs with Query-friendly Compression
P Fei, Z Li, Z Wang, X Yu, D Li, K Jee
In Proceedings of Usenix Security, Aug 2021 - APTrace: A Responsive System for Agile Enterprise Level Causality Analysis
J Gui, D Li, Z Chen, J Rhee, X Xiao, M Zhang, K Jee, Z Li, and H Chen
In Proceedings of ICDE, May 2020 - You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis
Q. Wang, W. U. Hassan, D. Li, K. Jee, X. Yu, K. Zou, J. Rhee, Z. Chen, W. Cheng, C. A. Gunter, H. Chen
In Proceedings of NDSS, Feb 2020 - Countering Malicious Processes with End-point DNS Monitoring
S. Sivakorn, K. Jee, Y. Sun, L. Kort-Parn, Z. Li, C. Lumezanu, Z. Wu, L. Tang, D. Li
In Proceedings of NDSS, Feb 2019 - NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage
W. U. Hassan, S. Guo, D. Li, Z. Chen, K. Jee, Z. Li, A. Bates
In Proceedings of NDSS, Nov 2019 - NodeMerge: Template-Based Efficient Data Reduction For Big-Data Causality Analysis
Y. Tang, D. Li, Z. Li, M. Zhang, K. Jee, Z. Wu, J. Rhee, X. Xiao, F. Xu, Q. Li
In Proceedings of CCS, Nov 2018 - SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
P. Gao, X. Xiao, D. Li, Z. Li, K. Jee, Z. Wu, C. H. Kim, S. R. Kulkarni, P. Mittal
In Proceedings of Usenix Security Aug 2018 - AIQL: Enabling Efficient Attack Investigation from System Monitoring Data
P. Gao, X. Xiao, Z. Li, K. Jee, F. Xu, S. R. Kulkarni, P. Mittal
In Proceedings of Usenix ATC, Jul 2018 - Towards a timely causality analysis for enterprise security
Y. Liu, M. Zhang, D. Li, K. Jee, Z. Li, Z Wu, J Rhee, P Mittal
In Proceedings of NDSS, Feb 2018 - High fidelity data reduction for big data security dependency analyses
Z Xu, Z Wu, Z Li, K Jee, J Rhee, X Xiao, F Xu, H Wang, G Jiang
In Proceedings of CCS, Nov 2016 - ShadowReplica: Efficient Parallelization of Dynamic Data Flow Tracking
K. Jee, V. P. Kemerlis, A. D. Keromytis and G. Portokalidis
In Proceedings of ACM CCS, Nov 2013 - A General Approach for Efficiently Accelerating Software-based Dynamic Data Flow Tracking on Commodity Hardware
K. Jee, G. Portokalidis, V. P. Kemerlis, S. Ghosh, D. I. August, and A. D. Keromytis
In Proceedings of NDSS, Feb 2012