UPDF AI

HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements

Kacper Wardega,Max von Hippel,2 Authors,Wenchao Li

2023 · DOI: 10.48550/arXiv.2301.10704
Adaptive Agents and Multi-Agent Systems · 2 Citations

TLDR

This work proposes a two-pronged mitigation for plan-deviation attacks: an attack detection technique leveraging both the robots' local sensing capabilities to report observations of other robots and co-observation schedules generated by the CE, and a prevention technique where the CE issues horizon-limiting announcements to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan.

Abstract

In centralized multi-robot systems, a central entity (CE) checks that robots follow their assigned motion plans by comparing their expected location to the location they self-report. We show that this self-reporting monitoring mechanism is vulnerable to plan-deviation attacks where compromised robots don't follow their assigned plans while trying to conceal their movement by mis-reporting their location. We propose a two-pronged mitigation for plan-deviation attacks: (1) an attack detection technique leveraging both the robots' local sensing capabilities to report observations of other robots and co-observation schedules generated by the CE, and (2) a prevention technique where the CE issues horizon-limiting announcements to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan. On a large-scale automated warehouse benchmark, we show that our solution enables attack prevention guarantees from a stealthy attacker that has compromised multiple robots.