URCAD Sneak Peek: Leon Tang

Sat, 06 Apr 2024 13:32:22 -0400

Leon Tang, Information Systems

Mentor: Zhiyuan Chen, Information Systems

A survey of Deceptive Techniques in Mobile Autonomous Systems: Attacks and Defenses

Mobile Autonomous Systems refer to robots, ground vehicles, drones etc. that can move autonomously. Such a system relies on sensors such as cameras and GPS to learn the states of the system itself as well as its surroundings and often machine learning (such as computer vision) and AI for autonomous decision making (e.g., to navigate). Deception is a serious threat to securing such systems because both the input to sensors as well as the machine learning models used in decision making can be misled by injected deceptive information. This project conducts a comprehensive survey of existing deception-based attacks against mobile autonomous systems and identifies gaps in the existing research. Compared to existing surveys, our survey considers various degrees of sophistication of the attacking strategies and type of data being altered. Our work also identifies gaps of existing research such as relatively little work has been done on multi-stage attacks and attacks that influence long term decision making. Our work can be used to help better understand deception based attacks as well as developing better defense strategies.

Poster presentation, 10-11:30am

UC Ballroom