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)

A new grant awarded from the SecureAmerica Institute. Thanks TEES for support!
PASAN is accepted to Security 2021.
Chung Hwan Kim is a program committee member of ICDCS 2021. Submit your cool papers!
Vessels is accepted to SOCC 2020.

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.

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.

Shear Shear

The Shear project creates a secure environment for the least-authority execution of over-privileged applications that may be exploited by adversaries to launch privileged attacks.

CLUE CLUE

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.

CAFE CAFE

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 and seminar (see more)

recent publications (see more)

PASAN: Detecting Peripheral Access Concurrency Bugs within Bare-metal Embedded Applications (to appear)
Taegyu Kim, Vireshwar Kumar, John Junghwan Rhee, Jizhou Chen, Kyungtae Kim, Chung Hwan Kim, Dongyan Xu, and Dave (Jing) Tian
In Security 2021 [ bibtex ]
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 [ pdf :: slides :: 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 [ pdf :: 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 ]