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

Abstract: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.

Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

C.2.0; C.2.3; I.2.6

arXiv:2608.02627 [cs.CR]

(or arXiv:2608.02627v1 [cs.CR] for this version)

Proceedings of the 2025 Artificial Intelligence and Smart Technologies for Sustainability Conference (AISTS), Rajkot, India, 21-23 August 2025, Institute of Electrical and Electronics Engineers (IEEE), pp. 1-6

https://doi.org/10.1109/AISTS66100.2025.11233161 DOI(s) linking to related resources