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Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on

Fatma S Alrayes1, Mohammed Maray2, Asma Alshuhail3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific Reports
|January 27, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a Privacy-Preserving Statistical Learning with Optimization Algorithm for High-Dimensional Big Data Environments (PPSLOA-HDBDE) approach. It achieves 99.49% accuracy in intrusion detection, enhancing data security for Internet of Things (IoT) devices.

Area of Science:

  • * Computer Science
  • * Data Science
  • * Cybersecurity

Background:

  • * The proliferation of Internet of Things (IoT) devices generates vast, high-dimensional data, posing significant privacy and security challenges.
  • * Existing privacy-preserving machine learning (ML) solutions often rely on server assistance and struggle with collusion attacks and the dynamic nature of IoT environments.
  • * High-dimensional data in big data environments complicates privacy protection, risking the efficacy and accuracy of statistical methods.

Purpose of the Study:

  • * To present a novel Privacy-Preserving Statistical Learning with an Optimization Algorithm for a High-Dimensional Big Data Environment (PPSLOA-HDBDE) approach.
  • * To ensure data confidentiality and analytical efficacy in big data scenarios, particularly for IoT networks.
  • * To address the limitations of current server-assisted privacy solutions and enhance intrusion detection capabilities.
Keywords:
Big dataEnsemble modelHigh-dimensionalIntrusion detectionLinear scaling normalizationPrivacy-preserving

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Main Methods:

  • * Data preprocessing using linear scaling normalization (LSN).
  • * Dimensionality reduction via sand cat swarm optimizer (SCSO)-based feature selection (FS).
  • * Intrusion detection using an ensemble of temporal convolutional network (TCN), multi-layer auto-encoder (MAE), and extreme gradient boosting (XGBoost), with hyperparameter tuning via an improved marine predator algorithm (IMPA).

Main Results:

  • * The PPSLOA-HDBDE technique demonstrated superior performance in privacy preservation and analytical efficacy.
  • * Achieved a high accuracy of 99.49% in intrusion detection tasks.
  • * Experimental validation confirmed the effectiveness of the proposed approach over existing models.

Conclusions:

  • * The PPSLOA-HDBDE approach effectively balances data privacy and analytical accuracy in high-dimensional big data environments.
  • * The integration of advanced optimization and ensemble techniques provides a robust solution for securing IoT data.
  • * The proposed method offers a significant advancement in privacy-preserving statistical learning for big data applications.