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

Computer Science > Cryptography and Security [Submitted on 24 Jul 2026] Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics Ashly Joseph 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. Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) ACM classes: C.2.0; C.2.3; I.2.6 Cite as: arXiv:2608.02627 [cs.CR]   (or arXiv:2608.02627v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.02627 Focus to learn more Journal reference: 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 Related DOI: https://doi.org/10.1109/AISTS66100.2025.11233161 Focus to learn more Submission history From: Ashly Joseph [view email] [v1] Fri, 24 Jul 2026 22:16:36 UTC (952 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.CV cs.LG cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)