Implementation of Dynamic Microsegmentation for a Multi-Robot Network
A Texas A&M University research collaboration with ColorTokens on securing IoT and robotic systems.
Vishwam Raval†
Global Cyber Research Institute Texas A&M University
College Station, USA
Jaewon Kims†*
Global Cyber Research Institute Texas A&M University
College Station, USA
Sandip Roy
Global Cyber Research Institute Texas A&M University
College Station, USA
IoT and cyber-physical systems are becoming central to industrial, defense, and critical-infrastructure environments. But as these systems become more connected, a compromised device can create pathways for lateral movement, operational disruption, and physical-world impact.
In this report, the Global Cyber Research Institute at Texas A&M University and ColorTokens demonstrate a laboratory implementation of dynamic microsegmentation for a multi-robot network using ColorTokens Xshield.
The study shows how tag-based asset classification and automated policy enforcement can isolate compromised robotic nodes while preserving connectivity for healthy devices.
Read the report to learn how dynamic microsegmentation was tested in a controlled multi-robot IoT environment.
- How microsegmentation supports zero-trust enforcement in IoT networks
- How infected robot nodes were moved into a quarantine segment
- How Xshield API-driven automation helped reduce lateral movement risk
- How healthy robotic nodes continued operating during threat isolation
Access the report to explore the full experiment, methodology, results, and future research direction.
†These authors contributed equally to this work. Copyright 2026 IEEE.
∗Corresponding author: Jaewon Kim ([email protected]).