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Related Experiment Videos

Intelligent cybersecurity management in industrial IoT system using attribute reduction with collaborative deep

Ibrahim Alrashdi1, Salahaldeen Duraibi2

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, 73211, Sakaka, Al-Jouf, Saudi Arabia.

Scientific Reports
|December 14, 2025
PubMed
Summary

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This summary is machine-generated.

The Intelligent Management of False Data Injection Attacks Using Feature Selection and Voting Classifier (IMFDIA-FSVC) technique effectively detects and mitigates false data injection attacks (FDIAs) in Industrial Internet of Things (IIoT) systems, achieving 99.15% accuracy.

Area of Science:

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

Background:

  • Industrial Internet of Things (IIoT) systems face significant security risks from False Data Injection Attacks (FDIAs).
  • Conventional security models struggle to detect FDIAs due to the dynamic nature of attackers and data manipulation.
  • Deep learning (DL) offers advanced capabilities for real-time FDIA detection and personalized security measures.

Purpose of the Study:

  • To introduce an Intelligent Management of False Data Injection Attacks Using Feature Selection and Voting Classifier (IMFDIA-FSVC) technique for IIoT systems.
  • To develop a robust model for detecting and mitigating FDIAs, ensuring secure and reliable IIoT operations.
  • To enhance the accuracy and efficiency of FDIA detection compared to existing methods.

Main Methods:

Keywords:
CybersecurityDeep learningFalse data injection attacksFeature selectionIndustrial internet of things

Related Experiment Videos

  • Data pre-processing including missing value analysis and normalization.
  • Feature selection using a statistical and information-theoretic selection (SITS) technique.
  • Classification employing Temporal Convolutional Network (TCN), Deep Belief Network (DBN), and Autoencoder (AE) with a voting classifier ensemble.

Main Results:

  • The IMFDIA-FSVC technique demonstrated superior performance in detecting FDIAs.
  • Achieved an accuracy of 99.15% on IIoT and FDIA datasets.
  • Outperformed existing models in the comparative study.

Conclusions:

  • The IMFDIA-FSVC technique provides an effective solution for FDIA detection and mitigation in IIoT.
  • The proposed method ensures safer and more trustworthy operations within IIoT environments.
  • The combination of advanced DL models and feature selection significantly improves detection accuracy.