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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

Portuguese

Implementações modernas da Internet Industrial das Coisas (IIoT) agora conectam gateways de borda, painéis em nuvem e aplicativos de negócios. Isso melhora a visibilidade e monitoramento remoto. No entanto, expõe simultaneamente o tráfego industrial a várias ameaças cibernéticas, incluindo varreduras, ataques web e injeção de linguagem de consulta estruturada (SQL), roubo de credenciais impulsionado por phishing e atividade de comando e controle (C2). Embora as ferramentas baseadas em regras permaneçam úteis para detectar assinaturas conhecidas, sua eficácia diminui com ataques minimamente alterados ou quando os alertas exigem investigação manual.

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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