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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

German

Moderne industrielle Internet der Dinge (IIoT) Implementationen verbinden jetzt Edge-Gateways, Cloud-Dashboards und Business-Anwendungen. Dies verbessert die Sichtbarkeit und die Fernüberwachung. Jedoch stellt es gleichzeitig industriellen Traffic verschiedenen Cyberbedrohungen wie Scannen, Webangriffen und Structured Query Language (SQL) Injection (SQLi), Phishing-gesteuerten Anmeldeinformationsdiebstahl und Kommando-und-Kontrolle (C2) Aktivität aus. Obwohl regelbasierte Tools für die Erkennung bekannter Signaturen nützlich bleiben, nimmt ihre Wirksamkeit mit minimalen Änderungen von Angriffen oder wenn Warnungen manuelle Untersuchung erfordern ab.

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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