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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

Japanese

現代の産業用物のインターネット(IIoT)の実装は今、エッジゲートウェイ、クラウドダッシュボード、ビジネスアプリケーションを接続しています。これは視覚性とリモートモニタリングを向上させます。しかし、同時に、スキャン、ウェブ攻撃、構造化クエリ言語(SQL)注入(SQLi)などのさまざまなサイバー脅威に産業トラフィックを曝露します。この論文は、IIoTのモニタリングと応答のための効率的な枠組みであるAIRO(Autonomous Intelligence-Driven Cybersecurity Risk Reaction System)を提示します。ルールベースのツールは既知の署名を検出するのにまだ有用ですが、その効性は最小限に変更

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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