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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption

Turki Aljrees1, Ankit Kumar2, Kamred Udham Singh3

  • 1Department College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al-Batin 39524, Saudi Arabia.

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Summary

This study introduces a novel approach combining efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning to enhance Internet of Things (IoT) security against random attacks. The integrated methods significantly reduce communication costs and improve analytical capabilities for robust IoT defense.

Keywords:
Internet of ThingsMITMcommunication costdata encryptionidentity-based online/offlinequondam signature algorithm (QSA)

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Internet of Things (IoT) systems face increasing random attacks, necessitating advanced security measures.
  • Existing security protocols often struggle with privacy concerns and communication overhead.
  • Federated learning offers a decentralized approach to enhance security and privacy in IoT.

Purpose of the Study:

  • To introduce a novel paradigm integrating efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning for IoT security.
  • To mitigate vulnerabilities associated with man-in-the-middle attacks and random threats.
  • To optimize communication costs and enhance analytical capabilities in IoT systems.

Main Methods:

  • Implementation of efficient data encryption techniques.
  • Utilization of the Quondam Signature Algorithm (QSA) for secure communication and attack mitigation.
  • Application of federated learning for decentralized model training and data aggregation while preserving privacy.
  • Comparative analysis of communication cost schemes, including encryption and federated learning facets.
  • Optimization of time complexity using an elliptic curve digital signature algorithm-based online/offline scheme.

Main Results:

  • The proposed approach demonstrates significant cost savings in IoT communication by optimizing bit requirements.
  • Federated learning enables secure aggregation and analysis of data from diverse devices, enhancing analytical prowess.
  • The Quondam Signature Algorithm (QSA) effectively mitigates man-in-the-middle attack vulnerabilities.
  • The integrated scheme shows improved efficiency and reduced communication costs compared to traditional methods like the Slow Block Move (SBM) scheme.
  • The research highlights enhanced resilience against a spectrum of IoT attacks.

Conclusions:

  • The synergistic integration of federated learning and efficient encryption provides a robust defense mechanism for IoT systems.
  • The proposed paradigm offers a marked reduction in communication costs and elevated analytical capabilities.
  • The approach enhances the overall security and resilience of IoT systems against evolving threats.