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AIRO: A Streaming-Based Machine Learning Framework for Threat Detection and Security in Industrial Internet of Things

Raima Deb, Pranav M Pawar, Mithun Mukherjee, Raja Muthalagu, Mian Guo · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30713 · open-access PDF

French

Les implémentations modernes d'Internet des objets industriels (IIoT) connectent désormais des passerelles de bord, des tableaux de bord cloud et des applications commerciales. Cela améliore la visibilité et la surveillance à distance. Cependant, il expose simultanément le trafic industriel à diverses menaces informatiques, y compris la numérisation, les attaques web et l'injection de langue de requête structurée (SQL), le vol d'informations basé sur le phishing et l'activité command-and-control (C2). Bien que les outils basés sur des règles restent utiles pour détecter des signatures connues, leur efficacité diminue avec des attaques minimalement modifiées ou lorsque les alertes nécessitent une enquête manuelle.

Machine translation (Meta M2M100); the English original follows.

Abstract

Modern Industrial Internet of Things (IIoT) im- plementations now connect edge gateways, cloud dashboards and business applications. This improves visibility and remote monitoring. However it simultaneously exposes industrial traffic to various cyber threats including scanning, web attacks and Structured Query Language (SQL) injection (SQLi), phishing- driven credential theft, and command-and-control (C2) activity. Although rule-based tools remain useful for detecting known signatures, their efficacy decreases with minimally altered attacks or when alerts require manual investigation. This pap

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