# Towards a Zero-Trust Micro-segmentation Network Security Strategy: An Evaluation Framework

arXiv: https://arxiv.org/abs/2111.10967 · HTML: https://mlsanity.com/paper/2111.10967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things](https://mlsanity.com/paper/2505.23792/) (2025)
- [Pro-ZD: A Transferable Graph Neural Network Approach for Proactive Zero-Day Threats Mitigation](https://mlsanity.com/paper/2602.07073/) (2026)
- [Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics](https://mlsanity.com/paper/2608.02627/) (2026)

## Abstract (arXiv)

Micro-segmentation is an emerging security technique that separates physical networks into isolated logical micro-segments (workloads). By tying fine-grained security policies to individual workloads, it limits the attacker's ability to move laterally through the network, even after infiltrating the perimeter defences. While micro-segmentation is proved to be effective for shrinking enterprise networks attack surface, its impact assessment is almost absent in the literature. This research is dedicated to developing an analytical framework to characterise and quantify the effectiveness of micro-segmentation on enhancing networks security. We rely on a twofold graph-feature based framework of the network connectivity and attack graphs to evaluate the network exposure and robustness, respectively. While the former assesses the network assets connectedness, reachability and centrality, the latter depicts the ability of the network to resist goal-oriented attackers. Tracking the variations of formulated metrics values post the deployment of micro-segmentation reveals exposure reduction and robustness improvement in the range of 60% - 90%.