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Related Experiment Video

Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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ChainShieldML an intelligent decentralized security framework for next generation wireless sensor networks.

Dileep Kumar Murala1, Shadab Ahmad2, V A Sankar Ponnapalli3

  • 1Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, 501203, Telangana, India.

Scientific Reports
|December 2, 2025
PubMed
Summary

ChainShieldML offers a novel hybrid security architecture for Wireless Sensor Networks (WSNs). This system combines blockchain and machine learning for efficient, decentralized, and adaptive protection against insider threats in IoT applications.

Keywords:
Blockchain technologyInternet of Things (IoT)Intrusion detectionMachine learningSecurityWireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for next-generation Internet of Things (IoT) applications, enabling smart automation in critical sectors.
  • WSNs face significant security challenges due to inherent limitations like low power, limited computation, and susceptibility to insider threats.
  • Traditional security methods are often insufficient for resource-constrained WSNs, necessitating innovative solutions.

Purpose of the Study:

  • To introduce ChainShieldML, a lightweight hybrid security architecture for WSNs.
  • To enhance the security, trustworthiness, and data integrity of WSNs against sophisticated attacks.
  • To provide a resource-efficient and adaptive defense mechanism suitable for constrained IoT environments.

Main Methods:

  • A two-pronged defense strategy integrating a permissionless blockchain for decentralized trust and identity verification with a machine learning module for threat detection.
  • Utilized a blockchain prevention module with smart contracts on the Ethereum ecosystem and the VBFT consensus algorithm for secure node registration and immutable logging.
  • Employed the LightGBM (Light Gradient Boosting Machine) algorithm for real-time detection and ranking of malicious nodes, optimizing for performance metrics like recall and inference latency.

Main Results:

  • ChainShieldML demonstrated significant improvements in detecting insider attacks and enhancing data protection within WSNs.
  • The architecture achieved high efficiency in terms of energy consumption and communication delay, crucial for resource-limited WSNs.
  • Performance evaluations confirmed LightGBM as an optimal machine learning classifier for WSN security, balancing accuracy and speed.

Conclusions:

  • ChainShieldML presents a novel, resource-efficient, and future-ready security solution for WSNs by synergizing blockchain and machine learning.
  • The hybrid approach effectively addresses the unique security vulnerabilities of WSNs, fostering decentralized trust and adaptive intelligence.
  • This architecture is vital for ensuring the reliable operation and security of WSNs in critical IoT applications.