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  1. Home
  3. Toward A Hybrid Intrusion Detection Framework For Iiot Using A Large Language Model.
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  3. Toward A Hybrid Intrusion Detection Framework For Iiot Using A Large Language Model.

Related Experiment Video

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Toward a Hybrid Intrusion Detection Framework for IIoT Using a Large Language Model.

Musaad Algarni1, Mohamed Y Dahab1, Abdulaziz A Alsulami2

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|February 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel intrusion detection system (IDS) for Industrial Internet of Things (IIoT) security. The hybrid framework enhances cybersecurity by combining text and numerical data, achieving high accuracy and preventing data leakage.

Keywords:
Industrial Internet of Things (IIoT)Large Language Model (LLM)Principal Component Analysis (PCA)classification (CLS)intrusion detection system (IDS)

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Area of Science:

  • Cybersecurity
  • Industrial Internet of Things (IIoT)
  • Machine Learning

Background:

  • Industrial Internet of Things (IIoT) connectivity enhances efficiency but introduces significant cybersecurity risks.
  • Existing intrusion detection systems (IDS) face challenges in IIoT due to data heterogeneity, high dimensionality, class imbalance, and data leakage.
  • Effective IDS are crucial for securing IIoT environments against evolving cyber threats.

Purpose of the Study:

  • To propose a leakage-safe, hybrid intrusion detection framework for Industrial Internet of Things (IIoT) environments.
  • To integrate text-based and numerical network flow features for robust threat detection.
  • To address challenges like data heterogeneity, high dimensionality, and class imbalance in IIoT security.

Main Methods:

  • Network flows converted to text descriptions and encoded using Bidirectional Encoder Representations from Transformers (BERT) for semantic embeddings.
  • Numerical traffic features standardized and combined with LLM embeddings.
  • Class prototypes computed in Principal Component Analysis (PCA) space, with cosine similarity scores added.
  • Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, Random Forest (RF) for feature selection, and Histogram-based Gradient Boosting (HGB) for classification.

Main Results:

  • The hybrid framework achieved 98.19% accuracy on the Edge-IIoTset dataset.
  • The framework demonstrated 99.15% accuracy on the ToN_IoT dataset.
  • The proposed method proved robust and leakage-safe in evaluating IIoT network traffic.

Conclusions:

  • The developed leakage-safe hybrid IDS framework effectively detects intrusions in IIoT environments.
  • Combining LLM-based text embeddings with numerical features enhances detection capabilities.
  • The framework offers a promising solution for securing interconnected industrial systems against cyber threats.