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A novel intrusion detection framework for optimizing IoT security.

Abdul Qaddos1, Muhammad Usman Yaseen1, Ahmad Sami Al-Shamayleh2

  • 1Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan.

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This study introduces a hybrid CNN-GRU model for Internet of Things (IoT) intrusion detection systems (IDS), achieving high accuracy. The novel approach effectively handles imbalanced data and enhances IoT security against evolving threats.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • The expanding Internet of Things (IoT) ecosystem requires advanced security measures.
  • Existing intrusion detection systems (IDS) face challenges with adaptability and IoT-specific complexities.
  • Imbalanced datasets are a common issue in developing effective IDS.

Purpose of the Study:

  • To propose a novel hybrid deep learning model for IoT intrusion detection.
  • To enhance the adaptability and accuracy of intrusion detection in IoT environments.
  • To address the challenge of imbalanced datasets in IoT security.

Main Methods:

  • Hybridization of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) architectures.
  • Integration of Feature-Weighted Synthetic Minority Oversampling Technique (FW-SMOTE) for data balancing.
  • Validation on IoTID20 and UNSW-NB15 datasets.

Main Results:

  • Achieved 99.60% accuracy in attack detection on the IoTID20 dataset.
  • Demonstrated 99.16% accuracy on the diverse UNSW-NB15 network dataset.
  • Outperformed existing benchmarks in IoT intrusion detection.

Conclusions:

  • The proposed hybrid CNN-GRU model offers a robust and adaptable solution for IoT security.
  • The approach effectively handles complex IoT data and imbalanced datasets.
  • This study sets new benchmarks for accuracy and versatility in safeguarding IoT ecosystems.