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Blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement protocol for edge-centric

Vijay Karnatak1, Neha Tripathi1, Mohammad Wazid1

  • 1Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India.

Scientific Reports
|May 29, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces BMAS-EIoT, a novel scheme for edge computing Internet of Things (IoT) systems. It enhances security through blockchain, machine learning, and authenticated key agreement, improving intrusion resilience.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Edge computing in Internet of Things (IoT) systems processes data locally to minimize latency and enable real-time decision-making.
  • Applications of edge-based IoT span smart cities, healthcare, industrial automation, and smart farming.
  • Existing IoT security schemes face challenges in authentication, key management, and intrusion detection within edge environments.

Purpose of the Study:

  • To propose a novel blockchain-driven, machine learning-enabled, intrusion-resilient authenticated key agreement scheme for edge-centric IoT systems (BMAS-EIoT).
  • To enhance the security, authentication, key management, and intrusion detection capabilities of edge-based IoT systems.
  • To provide network and threat models for better understanding system organization, deployment, and potential vulnerabilities.
Keywords:
AuthenticationBlockchainEdge computingInternet of thingsIntrusion detectionSecurity

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Main Methods:

  • Development of the BMAS-EIoT scheme integrating blockchain, machine learning, and authenticated key agreement.
  • Definition of network and threat models specific to edge-centric IoT environments.
  • Security analysis through investigation of potential attacks and performance comparison with existing schemes.
  • Practical implementation and evaluation of key components like intrusion detection and blockchain integration.

Main Results:

  • BMAS-EIoT demonstrates robust protection against a variety of potential security attacks.
  • The proposed scheme exhibits superior performance compared to current state-of-the-art solutions.
  • Practical implementation validates the effectiveness of the intrusion detection and blockchain components.
  • BMAS-EIoT offers enhanced security attributes and supplementary noteworthy capabilities.

Conclusions:

  • BMAS-EIoT provides a secure and efficient solution for edge-centric IoT systems, addressing critical security challenges.
  • The integration of blockchain and machine learning significantly improves intrusion resilience and key management.
  • The scheme's practical implementation confirms its viability and effectiveness in real-world scenarios.
  • BMAS-EIoT represents a significant advancement in securing the expanding landscape of edge computing IoT.