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S3 Lab - Software & Systems Security Laboratory
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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.

Recent News

Available Work

  • SensorFuzz: A fuzzing tool to test the resilience of robotic vehicles against sensor spoofing attacks (poster)
  • Mayday: A post-mortem analysis framework for robotic aerial vehicle accidents (paper)
  • RVFuzzer: A fuzzing tool to find control-semantic bugs in robotic vehicles (paper)
  • Minion: A framework for enforcing memory isolation on real-time microcontrollers (paper)

Acknowledgments

This project is supported by the University of Texas at Dallas Office of Research through the NFRS program.

current people

Minkyung Park
Minkyung Park
Post-doctoral associate
Zelun Kong
Zelun Kong
PhD student
Nate Simmons
Nate Simmons
Masters student

publications

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 ]
RVFuzzer: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Testing
Taegyu Kim, Chung Hwan Kim, Junghwan Rhee, Fan Fei, Zhan Tu, Gregory Walkup, Xiangyu Zhang, Xinyan Deng, and Dongyan Xu
In Security 2019 [ pdf :: slides :: bibtex ]
Securing Real-Time Microcontroller Systems through Customized Memory View Switching
Chung Hwan Kim, Taegyu Kim, Hongjun Choi, Zhongshu Gu, Xiangyu Zhang, and Dongyan Xu
In NDSS 2018 [ pdf :: slides :: bibtex ]