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Related Experiment Videos

TrustFed-RHIO: an optimization-driven differential privacy federated learning framework for secure and explainable

Linda Joseph1, B Prabha2, M Sambath2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vandalur-Kelambakkam Road, Chennai, 600127, Tamil Nadu, India. linda.joseph@vit.ac.in.

Scientific Reports
|May 4, 2026
PubMed
Summary

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Kohlraush’s Law and its Applications01:29

Kohlraush’s Law and its Applications

240
 Kohlrausch's law explains that at infinite dilution, where dissociation is complete, each ion's contribution to the conductivity of the electrolyte is independent of the nature of other ions present in the solution. It also implies that when an electrolyte is highly diluted, the conductance of the electrolyte is the sum of the individual conductances of the ions it generates upon dissociation. The quantity of electricity an ion carries is proportional to its molar ionic conductance, which...
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This study introduces TrustFed-RHIO, a hybrid model for industrial internet of things (IIoT) security. It enhances cyberattack detection accuracy and privacy preservation using rock hyrax intelligence optimization and federated learning.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Industrial Internet of Things (IIoT) systems face increasing cyberattack vulnerabilities.
  • Intelligent and privacy-preserving security solutions are crucial for interconnected IIoT devices.

Purpose of the Study:

  • To present TrustFed-RHIO, a novel hybrid model for enhanced IIoT security.
  • To improve collaborative cyberattack detection while preserving user privacy.

Main Methods:

  • Integrated rock hyrax intelligence optimization (RHIO) for optimal feature selection.
  • Employed a trustworthy differential privacy-enhanced federated learning (TrustFed) scheme for distributed learning.
  • Incorporated explainable AI (XAI) techniques (SHAP, LIME) for model interpretability.
Keywords:
Attack detectionCloud storageExplainable artificial intelligence (XAI)Industrial internet of thingsRock hyrax intelligence optimizationTrustworthy differential privacy-enhanced federated learning (TrustFed)

Related Experiment Videos

Main Results:

  • TrustFed-RHIO demonstrated superior performance in detection accuracy, robustness against adversarial attacks, and privacy preservation.
  • The model achieved high performance across various metrics including accuracy, precision, recall, and F1-score on the CCIoT2024-DIAD dataset.
  • The framework supports secure cloud storage for scalable real-world IIoT deployment.

Conclusions:

  • TrustFed-RHIO effectively detects IIoT cyberattacks.
  • The proposed model offers a secure, efficient, and interpretable solution for IIoT security challenges.
  • The hybrid approach enhances collaborative detection capabilities and mitigates data leakage risks.