Federated learning with LSTM and error correcting codes for secure and private identification of IoT devices

  • 0Computer Science Department, College of Science, Majmaah University, 11932, Al-Zulfi, Saudi Arabia. shaya@mu.edu.sa.

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Summary

This summary is machine-generated.

This study presents FL-HDECOC, a novel framework for secure Internet-of-Things (IoT) device identification. It enhances privacy and accuracy in heterogeneous IoT environments using federated learning and differential privacy.

Area Of Science

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background

  • The proliferation of Internet-of-Things (IoT) devices increases connectivity but also expands the attack surface, raising significant privacy and security concerns.
  • Accurate device identification is crucial for network security and managing heterogeneous IoT environments.

Purpose Of The Study

  • To introduce FL-HDECOC, a federated learning-based framework for privacy-preserving device identification in heterogeneous IoT settings.
  • To enhance the security and privacy of IoT networks through robust device identification methods.

Main Methods

  • The FL-HDECOC framework combines Long Short-Term Memory (LSTM) networks for temporal modeling with Error-Correcting Output Codes (DECOC) for multi-class classification.
  • A federated learning architecture ensures data locality and user privacy, with differential privacy further protecting shared model updates.
  • The model is designed for heterogeneous IoT environments, addressing the challenges of diverse device types and network conditions.

Main Results

  • FL-HDECOC achieved 96.2% accuracy and a 95.5% F1-score in experimental evaluations.
  • The framework demonstrated a 25% reduction in communication rounds compared to baseline models under a privacy budget of ε=1.0.
  • Performance evaluations confirmed the model's effectiveness in outperforming existing methods.

Conclusions

  • FL-HDECOC offers a promising solution for secure and scalable device identification in IoT deployments.
  • The integration of federated learning, LSTM, DECOC, and differential privacy effectively addresses privacy and accuracy challenges.
  • The framework contributes to enhancing the overall security posture of interconnected IoT systems.

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