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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Related Experiment Video

Updated: May 3, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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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
PubMed
Summary

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)

Related Experiment Videos

Last Updated: May 3, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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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.