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Related Experiment Videos

A blockchain-based secure data transmission framework in IoT using adaptive deep network with optimized cryptography

Anguraju Krishnan1, Rajesh Arunachalam2, M P Rajakumar3

  • 1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, 602105, Tamil Nadu, India.

Scientific Reports
|May 11, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a secure Internet of Things (IoT) data transmission method using advanced AI for intrusion detection and cryptography for secure communication. The novel approach significantly enhances IoT data security and communication efficiency.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) devices generate vast amounts of data, posing significant security challenges.
  • Existing security protocols often struggle with the dynamic and resource-constrained nature of IoT environments.
  • Robust intrusion detection and secure data transmission are critical for reliable IoT operation.

Purpose of the Study:

  • To develop a comprehensive security framework for IoT data transmission.
  • To enhance intrusion detection capabilities within IoT networks.
  • To improve the efficiency and security of data encryption in IoT.

Main Methods:

  • Utilized an Adaptive and Sparse Attention-based Dense Long Short-Term Memory (ASA-DLSTM) network for intrusion detection.
Keywords:
Adaptive and sparse attentionBlockchainDense long short-term memoryInternet of thingsOptimal key-based elliptic galois cryptographySecure communicationSorted fitness-based addax optimization algorithm

Related Experiment Videos

  • Employed the Sorted Fitness-based Addax Optimization Algorithm (SF-AOA) for adaptive parameter optimization of the ASA-DLSTM model.
  • Implemented Optimal Key-based Elliptic Galois Cryptography (OK-EGC) for secure data transmission, integrating Elliptic and Galois fields with optimized key management.

    • Main Results:

    • The proposed SF-AOA-ASA-DLSTM technique achieved an accuracy of 95.97% in intrusion detection, outperforming existing methods like DNN (83.77%) and SVM (83.19%).
    • The OK-EGC method demonstrated enhanced encryption efficiency and security by combining advanced cryptographic fields and optimized key strategies.
    • The integrated system effectively addressed critical IoT data security challenges, providing robust protection.

    Conclusions:

    • The developed framework offers a significant advancement in securing IoT data transmission.
    • The combination of AI-driven intrusion detection and advanced cryptography provides a powerful solution for IoT security.
    • The proposed model demonstrates high accuracy and efficiency, making it suitable for real-world IoT applications.