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Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics — arXiv:2608.02627

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.02627
License (as archived): CC0-1.0
Archived: 2026-10-31T12:34:18.280Z
SHA-256 of the archived text: 58bd4f5b1d0805ceb46d48ca18d5212271f8618ebe376cd3b2d7495d8e4a64f9
Stable URL of this page: https://metraipolis.com/documento/dc_d93fcdf241ec

How to cite

Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics — arXiv:2608.02627 — arXiv (en), https://arxiv.org/abs/2608.02627 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d93fcdf241ec (Metraipolis archive dc_d93fcdf241ec, sha256 58bd4f5b1d0805ceb46d48ca18d5212271f8618ebe376cd3b2d7495d8e4a64f9)

BibTeX

@misc{dcd93fcdf241ec,
  title = {Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics — arXiv:2608.02627},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.02627},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d93fcdf241ec; sha256 58bd4f5b1d0805ceb46d48ca18d5212271f8618ebe376cd3b2d7495d8e4a64f9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.02627. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: fiducia digitale e crittografia (retro-catalogo arXiv: annunci del 2026-08-05 in cs.CR).
Categorie arXiv: Cryptography and Security (cs.CR) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2608.02627 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:21:50.537Z dalla pagina https://arxiv.org/catchup/cs.CR/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).