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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
Summary
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.
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.
