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<p>The process of securing IoT networks is large, particularly because the older Intrusion Detection Systems (IDS) are often unable to perform with accuracy, speed, and user privacy protection. This paper presents a hybrid Intrusion Detection System (IDS) which is an integration of powerful tools (Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders), and Blockchain technology to develop a more efficient solution. We show that our system is better than existing techniques with F1 score of 99.71, 99.99% accuracy, 99.68% precision, and 99.75% recall. The most important challenges of privacy are addressed with the help of Blockchain that provides safe data processing and prevents manipulation. With deep learning algorithms, the system can identify even sophisticated patterns of attack. In the future, we will bring it closer to real-time and scalable as well as equip it with adversarial learning so that it can respond to new categories of cyber threats. This study illustrates that a blend of superior technologies can improve security, effectiveness, and confidentiality of IoT networks.</p>

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Hybrid Intrusion Detection Systems for IoT Networks Using Blockchain, Deep Learning, and Privacy-Preserving Mechanisms

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Sunil Kumar Alavilli,
  • Chaitanya Vasamsetty,
  • Bhavya Kadiyala,
  • Rajani Priya Nippatla,
  • Subramanyam Boyapati,
  • Nisar Ahmed

摘要

The process of securing IoT networks is large, particularly because the older Intrusion Detection Systems (IDS) are often unable to perform with accuracy, speed, and user privacy protection. This paper presents a hybrid Intrusion Detection System (IDS) which is an integration of powerful tools (Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders), and Blockchain technology to develop a more efficient solution. We show that our system is better than existing techniques with F1 score of 99.71, 99.99% accuracy, 99.68% precision, and 99.75% recall. The most important challenges of privacy are addressed with the help of Blockchain that provides safe data processing and prevents manipulation. With deep learning algorithms, the system can identify even sophisticated patterns of attack. In the future, we will bring it closer to real-time and scalable as well as equip it with adversarial learning so that it can respond to new categories of cyber threats. This study illustrates that a blend of superior technologies can improve security, effectiveness, and confidentiality of IoT networks.