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A Deployment-Aware Feasibility Framework for Machine Learning-Based IoT Intrusion Detection Across Edge, Fog, and Cloud Architectures — arXiv:2610.08867

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08867
License (as archived): CC0-1.0
Archived: 2026-10-08T11:44:18.430Z
SHA-256 of the archived text: 86ff301ac39ed92d97db13b01b0b37544a32948d988e3acdf6be4b9c26bb5860
Stable URL of this page: https://metraipolis.com/documento/dc_ec87f0325090

How to cite

A Deployment-Aware Feasibility Framework for Machine Learning-Based IoT Intrusion Detection Across Edge, Fog, and Cloud Architectures — arXiv:2610.08867 — arXiv (en), https://arxiv.org/abs/2610.08867 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec87f0325090 (Metraipolis archive dc_ec87f0325090, sha256 86ff301ac39ed92d97db13b01b0b37544a32948d988e3acdf6be4b9c26bb5860)

BibTeX

@misc{dcec87f0325090,
  title = {A Deployment-Aware Feasibility Framework for Machine Learning-Based IoT Intrusion Detection Across Edge, Fog, and Cloud Architectures — arXiv:2610.08867},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08867},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec87f0325090; sha256 86ff301ac39ed92d97db13b01b0b37544a32948d988e3acdf6be4b9c26bb5860},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08867. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The rapid growth and heterogeneity of Internet of Things (IoT) environments have exposed fundamental limitations in traditional rule-based and signature-based intrusion detection systems. This paper presents a quantitative deployment-aware analysis of machine learning (ML)-based intrusion detection approaches across edge, fog/gateway, and cloud architectures. Unlike prior surveys that primarily emphasize detection accuracy, this work defines representative quantitative deployment capability envelopes extracted from experimental and system-level studies and introduces a structured Deployment Feasibility Score (DFS) model. The proposed framework maps ML techniques to architectural layers based on computational demand, memory footprint, and latency sensitivity using a weighted ordinal scoring mechanism. The analysis demonstrates that lightweight statistical and linear models are most suitable for edge deployment, ensemble and clustering-based methods align with fog/gateway environments, while deep and optimization-driven models are best suited for cloud infrastructures. By formalizing deployment feasibility through quantitative grounding and structured evaluation, this work provides practical guidance for selecting intrusion detection solutions under real-world architectural constraints, supporting more informed and deployment-conscious IoT security design.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2610.08867 — 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 2026-10-08T10:45:22.431Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).