Implementation of Dynamic Microsegmentation for a Multi-Robot Network

Vishwam Raval; Jaewon Kim; Ravi Pattabhi; Sandip Roy

eess.SYOct 21, 2026

Implementation of Dynamic Microsegmentation for a Multi-Robot Network

Authors: Vishwam Raval, Jaewon Kim, Ravi Pattabhi, Sandip Roy

Organizations: Global Cyber Research Institute Texas A&M University College Station, USA · ColorTokens Inc San Jose, CA

Abstract

This report describes a laboratory instantiation of a dynamic microsegmentation function for an Internet-of-Things network, specifically a multi-robot network, using a commercial software-defined microsegmentation tool. Fine-grained dynamic segmentation policies are defined, where segments are adapted based on asset functional and health tags. Automated implementation of the dynamic policies in software is also undertaken. The microsegmentation solution is shown to automatically isolate an indicated threat in real time, while maintaining unaffected operations. The demonstration indicates that microsegmentation can be implemented in IoT networks, in a way that allows for robust operation in the face of threats and disruptions.

Figures & tables

Fig. 1 : Configuration of Zero-Trust Microsegmentation and Connectivity for Networked Robot Application.

Fig. 2 : Network Router, Network Switch, and ColorTokens Microsegmentation device connected to the Raspberry Pi-powered Robots.

Fig. 3 : Zero-Trust Microsegmentation Automation Flowchart for Networked Robot Application.

Fig. 4 : Policy Automation for Microsegmentation Implementation.

Panel of Figure 5 — Fig. 5 : Initial Assets Allowed Connectivity.

Panel of Figure 6 — Fig. 6 : Attack Surface and Blast Radius after gchild1 , gchild2 , and gchild3 are determined to be infected.

Fig. 7 : Controller trying to connect with the Infected Robot, the ICMP and SSH traffic is denied.

Panel of Figure 8 — Fig. 8 : The network Attack Surface and Blast Radius during the recovery process by changing the infected tag and updating the segments.

Internet of Things

The Internet of Things (IoT) connects networked sensors, devices, and systems for sensing, communication, and automated action.

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Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics

Ashly Joseph

Network Intrusion Detection Cybersecurity Zero-Trust Security Anomaly Detection

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Zero Trust Architecture (ZTA) principles need rigorous network segmentation and ongoing verification to reduce implicit trust and lateral threat propagation. This paper investigates anomaly detection in software-defined networking (SDN) systems by micro-segmentation, using deep learning models to detect harmful actions that evade traditional coarse-grained monitoring. Two models are developed: a Vision Transformer (ViT) and a 1D Convolutional Neural Network (1D-CNN), which are used to both raw and micro-segmented network flow data. Experimental findings from a simulated zero-trust SDN dataset indicate that micro-segmentation substantially improves detection accuracy. The models trained on segmented input demonstrate enhanced accuracy and F1-scores (F1 = 0.95) compared to those utilizing unsegmented raw data (F1 = 0.90). The ViT-based detector marginally surpasses the 1D-CNN, particularly in recognizing nuanced lateral movement patterns that are unnoticed in unprocessed data. These findings highlight the significance of including micro-segmentation inside zero-trust networks to enhance intrusion detection efficacy. Future efforts will broaden this methodology to include extensive real-world network datasets and dynamic online segmentation techniques.

Abstract

Internet of Things

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