Open Collections

Wattoo, Zainab Saeed

University of British Columbia

A self-driving laboratory is a cyber-physical system that uses software-controlled laboratory equipment, such as robot arms and smart devices, to permit autonomous experimentation. Intelligent systems in these laboratories can independently conduct experiments, analyze results, and identify a subsequent experiment to run. However, self-driving laboratories are vulnerable to security attacks due to their dependence on networked communication. Further, a naive researcher could make human errors while prototyping new experiments. Both an attacker or a naive researcher could potentially create dangerous situations that pose risks to the safety and security of self-driving laboratories. For instance, they could make a robot arm crash into expensive equipment or launch dangerous experiments. We present Sarwat, a rule-based intrusion detection system (IDS) for self-driving laboratories, designed to prevent unsafe behavior. Sarwat uses a set of rules for defining the actions that are allowed in a self-driving laboratory. If an action inside the laboratory violates any of the rules, Sarwat raises an alarm. Sarwat achieves an overall detection rate of 75%, making it effective for most of the unsafe scenarios we identified. We conducted a pilot study to evaluate the user-friendliness of Sarwat and found that the initial setup of Sarwat in a self-driving laboratory requires our assistance. However, once configured, it is easy to maintain, making it valuable for training new users and prototyping new experiments. Additionally, Sarwat introduces minimal latency overhead of 1.5% to the ongoing experiment workflows of a self-driving laboratory. Therefore, Sarwat allows researchers in a self-driving laboratory to perform experiments safely and securely.

Text; Software

2023-09-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85889

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/