S3 Lab - Software & Systems Security Laboratory
South Engineering and Computer Science Building

about us

We conduct research and build practical tools to analyze the security of diverse software and develop dependable systems using program analysis, operating systems and cyber-physical systems techniques.

If you are interested in working with us, please fill out this form.

S3 Lab is part of the Cyber Security Research and Education Institute and the Department of Computer Science at the University of Texas at Dallas.

recent news (see more)

Vessels is accepted to SOCC 2020.
Chung Hwan Kim joins the Dept. of Computer Science at UT Dallas and starts S3 Lab.

highlighted projects (see more)

AI Vault AI Vault

The AI Vault project designs and develops a new trusted execution environment tailored to run artificial intelligence and machine learning programs on modern AI platforms (e.g., cloud and embedded devices) while providing strong data confidentiality and high efficiency.

AutoInsight AutoInsight

The AutoInsight project applies advanced techniques in software security and control systems to build a new security analysis platform for self-driving car systems.

RetroV RetroV

Robotic vehicles (as known as drones) are facing various threats of cyber-physical attacks that exploit their security vulnerabilities. RetroV develops automated analysis tools to find such vulnerabilities in existing robotic vehicle systems retrospectively and retrofits their design against advanced cyber-physical attacks.


The CLUE project develops an infrastructure to detect and diagnose system anomalies in enterprise systems. These anomalies include stealthy malware and other types of hidden system anomalies. CLUE provides a diverse set of tools to find and understand such anomalies with minimal disruption to the target system.


Cloud Application Function Enclave (CAFE) is an end-to-end framework for confidential distribution and execution of cloud applications. Attackers with a reverse-engineering capability may steal or manipulate sensitive application logic. CAFE prevents such attempts using hypervisor- and hardware-based techniques.

research areas

courses (see more)

recent publications (see more)

Vessels: Efficient and Scalable Deep Learning Prediction on Trusted Processors
Kyungtae Kim, Chung Hwan Kim, John Junghwan Rhee, Xiao Yu, Haifeng Chen, Dave (Jing) Tian, and Byoungyoung Lee
In SOCC 2020 [ bibtex ]
Detecting Malware Injection with Program-DNS Behavior
Yixin Sun, Kangkook Jee, Suphannee Sivakorn, Zhichun Li, Cristian Lumezanu, Lauri Korts-Pàˆrn, Zhenyu Wu, John Junghwan Rhee, Chung Hwan Kim, Mung Chiang, and Prateek Mittal
In EuroS&P 2020 [ bibtex ]
From Control Model to Program: Investigating Robotic Aerial Vehicle Accidents with Mayday
Taegyu Kim, Chung Hwan Kim, Altay Ozen, Fan Fei, Zhan Tu, Xiangyu Zhang, Xinyan Deng, Dave (Jing) Tian, and Dongyan Xu
In Security 2020 [ pdf :: slides :: bibtex ]
HFL: Hybrid Fuzzing on the Linux Kernel
Kyungtae Kim, Dae R. Jeong, Chung Hwan Kim, Yeongjin Jang, Insik Shin, and Byoungyoung Lee
In NDSS 2020 [ pdf :: slides :: bibtex ]
Progressive Processing of System Behavioral Query
Jiaping Gui, Xusheng Xiao, Ding Li, Chung Hwan Kim, and Haifeng Chen
In ACSAC 2019 [ pdf :: slides :: bibtex ]