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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 look at this page.

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)

DriveFuzz is (conditionally) accepted to CCS 2022.
Trusted Things is featured on TEES News.
Chung Hwan Kim is a program committee member of AutoSec 2022.
Chung Hwan Kim is a program committee member of SafeThings 2022.

highlighted projects (see more)

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.

Trusted Things Trusted Things

The Trusted Things project develops new software systems to enable secure IoT leveraging trusted execution environment techniques.

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.

PeriShield PeriShield

The PeriShield project analyzes the security of various types of peripheral devices and it develops cutting-edge tools to detect/prevent malicious peripherals.

research areas

courses and seminar (see more)

recent publications (see more)

PASAN: Detecting Peripheral Access Concurrency Bugs within Bare-metal Embedded Applications
Taegyu Kim, Vireshwar Kumar, Junghwan "John" Rhee, Jizhou Chen, Kyungtae Kim, Chung Hwan Kim, Dongyan Xu, and Dave (Jing) Tian
In Security 2021 [ pdf :: slides :: bibtex ]
Vessels: Efficient and Scalable Deep Learning Prediction on Trusted Processors
Kyungtae Kim, Chung Hwan Kim, Junghwan "John" 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, 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 ]