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A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning.

Raneem Qaddoura1, Ala' M Al-Zoubi2,3, Hossam Faris3,4

  • 1Information Technology, Philadelphia University, Amman 19392, Jordan.

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|April 30, 2021
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Summary

This study introduces a deep multi-layer classification approach for IoT intrusion detection, enhancing network security and user privacy. The proposed method significantly outperforms existing techniques in identifying malicious activities.

Keywords:
IoTID20SMOTEclassificationdeep learningimbalancedintrusion detectionneural networkoversampling

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Security

Background:

  • Internet of Things (IoT) network security is critical for service availability and user privacy.
  • Intrusion detection systems (IDS) are essential for identifying and mitigating network threats.
  • Existing IDS methods require improvement in accuracy and efficiency.

Purpose of the Study:

  • To propose a novel deep multi-layer classification approach for IoT intrusion detection.
  • To enhance the quality of classification results using an oversampling technique.
  • To optimize the performance of the proposed IDS by evaluating different settings.

Main Methods:

  • A two-stage deep learning model was developed for intrusion detection: first, detecting intrusion existence, and second, identifying intrusion type.
  • An oversampling technique was applied to balance the dataset and improve classification accuracy.
  • Experiments were conducted to determine optimal parameters for the Single-hidden Layer Feed-forward Neural Network (SLFN) and Long Short-Term Memory (LSTM) components, as well as the oversampling strategy.

Main Results:

  • The optimal configuration involved oversampling by the Intrusion Type Identification (ITI) label, 150 neurons for SLFN, and 2 layers with 150 neurons for LSTM.
  • The proposed deep multi-layer classification approach achieved a G-mean of 78%.
  • This performance surpasses established techniques like KNN (75% G-mean) and other methods (less than 50% G-mean).

Conclusions:

  • The proposed deep multi-layer classification approach offers superior performance for IoT intrusion detection.
  • The optimized model effectively enhances network security and protects user privacy against malicious activities.
  • This research provides a robust solution for improving the reliability of IoT networks.