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

• Sep. 7, 2022: SensorFuzz poster is accepted to CCS 2022
• Aug. 13, 2020: Mayday conference talk at Security 2020
• May 15, 2020: Mayday paper is accepted to Security 2020

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.