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September 10, 2025International Research Journal of Multidisciplinary ScopeOpen Access

Blockchain-Enabled Collaborative Threat Intelligence in IoT Security Using a Hybrid Neural Network Model

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Authors

PSPrasanna SimhadatiCRC. Kishor Kumar ReddyRGR. M. Gomathi

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Overview

This framework enhances IoT security through blockchain and ML, ensuring privacy in threat intelligence sharing.

Key Points

  • The CNNTransLSTM model detects and classifies threats in real-time, outperforming traditional methods.
  • Using smart contracts and blockchain, the framework guarantees the integrity and immutability of Cyber Threat Intelligence data.
  • The approach facilitates secure collaboration among stakeholders, enhancing the resilience of IoT ecosystems.
  • An iOS app allows users to report threats and receive alerts, improving human-machine interaction.

Cite This Study

Simhadati et al. (2025) studied this question.

synapsesocial.com/papers/68c1ad6354b1d3bfb60e5a0ahttps://doi.org/10.47857/irjms.2025.v06i03.04288
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem2024 · 35 citations
  2. 2A Novel Security Framework for IoT Networks using Blockchain Technology and Adaptive Residual LSTM with Attention Mechanism2026
  3. 3IoT Threat Mitigation: Leveraging Deep Learning for Intrusion Detection2024 · 5 citations
  4. 4A Unified Adaptive Cyber Threat Intelligence Model for Real-Time IoT Security Using Machine Learning and GAN-Based Augmentation2025
  5. 5A Secure Framework for IoT Applications Using Blockchain and Artificial Intelligence2025