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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment.

Faten Khalid Karim1, José Varela-Aldás2, Mohamad Khairi Ishak3

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

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PubMed
Summary

This study introduces a new Bayesian Machine Learning with Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique to enhance Industrial Internet of Things (IIoT) security. The BMLSSA-CAD method effectively detects cyberattacks, achieving high accuracy on benchmark datasets.

Keywords:
Bayesian machine learningChameleon optimization algorithmCyberattack detectionIndustrial internet of thingsSparrow search algorithm

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Area of Science:

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

Background:

  • The Industrial Internet of Things (IIoT) is rapidly expanding, connecting numerous devices and enabling automation.
  • Continuous connectivity makes IIoT systems vulnerable to sophisticated cyberattacks.
  • Existing Intrusion Detection Systems (IDS) require enhanced methods for effective cyberattack detection in IIoT.

Purpose of the Study:

  • To propose a novel technique, Bayesian Machine Learning with Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD), for robust security in IIoT networks.
  • To improve the accuracy and efficiency of cyberattack detection within IIoT environments.
  • To leverage advanced machine learning and optimization algorithms for enhanced IIoT security.

Main Methods:

  • Data preprocessing using min-max scaler normalization.
  • Feature selection via the Chameleon Optimization Algorithm (COA).
  • Cyberattack detection using a Bayesian Belief Network (BBN) model, with hyperparameter tuning by the Sparrow Search Algorithm (SSA).

Main Results:

  • The BMLSSA-CAD technique demonstrated high performance in detecting cyberattacks.
  • Achieved superior accuracy rates of 97.84% and 98.93% on the UNSWNB51 and UCI SECOM datasets, respectively.
  • Outperformed recent techniques in experimental validation for IIoT cyberattack detection.

Conclusions:

  • The proposed BMLSSA-CAD technique offers a significant advancement in IIoT security.
  • The integration of BBN, COA, and SSA provides an effective framework for detecting cyber threats.
  • This approach enhances the reliability and security of industrial systems against cyberattacks.