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Related Experiment Videos

BlockFedZTA: a trust-aware federated learning framework for secure multi-organizational intrusion detection.

Reem Alshenaifi1, Shailendra Mishra2, Shams Tahzib1

  • 1Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.

Scientific Reports
|July 2, 2026
PubMed
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BlockFedZTA enhances supply chain security with a privacy-preserving intrusion detection system. This federated learning framework ensures robust threat detection despite data drift and malicious attacks.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Supply Chain Management

Background:

  • Designing privacy-preserving intrusion detection systems (IDS) for supply chains is complex due to data privacy needs, varied data distributions, and unreliable nodes.
  • Existing methods struggle with data heterogeneity and potential malicious participants in federated learning environments.

Purpose of the Study:

  • To propose BlockFedZTA, a novel framework integrating federated learning, XGBoost, trust-aware aggregation, and integrity verification for privacy-preserving IDS in supply chains.
  • To evaluate the robustness and performance of BlockFedZTA against data drift and label poisoning attacks.

Main Methods:

  • BlockFedZTA utilizes federated learning where participants train local models and share only commitment hashes (SHA-256) to protect privacy.
Keywords:
Blockchain-Inspired LoggingFederated LearningIntrusion DetectionSupply Chain SecurityToN-IoT DatasetXGBoostZero-Trust Architecture

Related Experiment Videos

  • A trust-aware aggregation mechanism weights updates based on validation performance, mitigating low-quality or malicious contributions.
  • Integrity is verified using a lightweight commitment-based mechanism.

    • Main Results:

    • BlockFedZTA achieved high average accuracies (0.966, 0.964, 0.963) across no-drift, moderate-drift, and severe-drift scenarios.
    • The trust-aware aggregation significantly outperformed FedAvg under drift conditions (p < 0.01) and demonstrated superior performance (0.9647 accuracy) compared to other methods.
    • The framework showed high resistance to label poisoning (accuracy > 0.962 at 60% poisoning) and maintained performance with scalability up to 50 clients.

    Conclusions:

    • BlockFedZTA provides a robust and privacy-preserving federated intrusion detection system suitable for supply chain environments.
    • The trust-aware aggregation and integrity verification mechanisms enhance security and reliability against various threats and data variations.
    • The framework offers a practical solution for enhancing supply chain network security without compromising data privacy.