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

Raima Deb, Pranav Mothabhau Pawar, Mithun Mukherjee, Raja Muthalagu, Mian Guo · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30713 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Implementasi modern Industrial Internet of Things (IIoT) sekarang menghubungkan gateway edge, dashboard cloud dan aplikasi bisnis. Ini meningkatkan visibilitas dan pemantauan jarak jauh. Namun, secara simultan mengekspos lalu lintas industri ke berbagai ancaman cyber termasuk pemindaian, serangan web dan injeksi Structured Query Language (SQL) (SQLi), pencurian credential yang didorong oleh phishing, dan kegiatan command-and-control (C2) . Meskipun alat berbasis aturan tetap berguna untuk mendeteksi tanda tangan yang diketahui, efektivitas mereka menurun dengan serangan yang minimal berubah atau ketika peringatan memerlukan penyelidikan manual. Dokumen ini menyajikan AIRO (Autonomous Intelligence-Driven Cybersecurity Risk Reaction System), kerangka kerja yang efisien untuk pemantauan dan tanggapan IIoT

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 paper presents AIRO (Autonomous Intelligence-Driven Cybersecurity Risk Re- duction System), an efficient framework for IIoT monitoring and response. AIRO combines Apache Kafka streaming, attack- specific feature extraction, machine learning (ML) inference, confidence-based decision logic and risk reduction measures such as Internet Protocol (IP) blocking, account suspension and traffic limiting. We developed four specialized datasets for C2 beaconing, reconnaissance, SQLi, and phishing to evaluate AIRO. These datasets include noisy and overlapping characteristic distribu- tions to minimize label leakage. The best-performing models, primarily Gradient Boosting and LightGBM, achieve F1-scores up to 0.9229 while ensuring low-latency from event input to mitigation

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Topics: Computer science, Computer security, The Internet, Cloud computing, Credential, Machine learning

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