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Related Concept Videos

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Related Experiment Videos

A secure and lightweight cryptographic-machine learning framework for IoT-based cyber defense in resource-constrained

Gaurav Thakur1,2, Pradeep Chouksey2, Mayank Chopra2

  • 1Department of Computer Science and Engineering, Central University of Jammu, Samba, Jammu and Kashmir, 181143, India.

Scientific Reports
|June 26, 2026
PubMed
Summary

This study introduces a secure, lightweight hybrid framework for Internet of Things (IoT) cyber defense, combining cryptography and machine learning for efficient anomaly detection in resource-constrained environments.

Keywords:
ECCEdge computingIntrusion detectionIoT securityLightweight cryptographyMachine learningRandom forestSHA-3SPECK

Related Experiment Videos

Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Internet of Things (IoT) expansion presents significant security challenges, especially for resource-constrained devices.
  • Traditional security solutions are often impractical for IoT due to limited computational power and energy.
  • Need for efficient and lightweight security frameworks tailored for IoT environments.

Purpose of the Study:

  • To propose a secure and lightweight hybrid framework for IoT cyber defense.
  • To integrate cryptographic techniques with machine learning for anomaly detection.
  • To evaluate the framework's performance in a resource-constrained edge environment.

Main Methods:

  • Utilized Elliptic Curve Cryptography (ECC) for secure key exchange.
  • Employed SPECK for lightweight data encryption and SHA-3 for data integrity.
  • Integrated a Random Forest classifier for machine learning-based anomaly detection.
  • Implemented and evaluated the framework on a Raspberry Pi edge system using the CIC-BCCC-NRC-IoT-2023 dataset.

Main Results:

  • Achieved 89.5% accuracy and 90% F1-score in anomaly detection.
  • Demonstrated low average end-to-end latency of 1.08 ms.
  • Reported minimal energy consumption of approximately 4.5 mJ per inference.
  • Showcased a practical balance between security, efficiency, and performance.

Conclusions:

  • The proposed hybrid framework offers a viable solution for securing resource-constrained IoT systems.
  • The integration of ECC, SPECK, SHA-3, and Random Forest provides effective cyber defense.
  • Future research should focus on broader validation and large-scale deployment analysis.