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A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems.

Sahba Baniasadi1, Omid Rostami1, Diego Martín2

  • 1Department of Industrial Engineering, University of Houston, Houston, TX 77204, USA.

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|June 24, 2022
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Summary

This study introduces a new training algorithm, neighborhood search-based particle swarm optimization (NSBPSO), to enhance deep learning for Internet of Things (IoT) intrusion detection. The improved method boosts accuracy and performance in identifying network threats.

Keywords:
IoTdeep learningnetwork intrusion detectionoptimal network training

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) presents significant security challenges, with network intrusion detection being a critical concern.
  • Existing machine/deep learning methods for IoT intrusion detection show potential for improved accuracy and performance.
  • Optimizing deep learning model parameters is key to enhancing intrusion detection capabilities.

Purpose of the Study:

  • To develop a novel training algorithm for optimizing deep learning architectures used in network intrusion detection.
  • To introduce a neighborhood search-based particle swarm optimization (NSBPSO) algorithm to enhance the exploration and exploitation capabilities of particle swarm optimization (PSO).
  • To improve the accuracy and performance of an intrusion detection system by optimally training a deep architecture using the proposed NSBPSO algorithm.

Main Methods:

  • Development of a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm.
  • Application of NSBPSO to optimally train a deep learning architecture for network intrusion detection.
  • Evaluation of the proposed classifier using two benchmark datasets: UNSW-NB15 and Bot-IoT.

Main Results:

  • The NSBPSO algorithm effectively improves the parameter tuning of deep learning models.
  • The optimally trained deep architecture demonstrates enhanced accuracy and performance in network intrusion detection.
  • Validation on UNSW-NB15 and Bot-IoT datasets confirms the effectiveness of the proposed approach.

Conclusions:

  • The proposed NSBPSO-based training algorithm offers a significant advancement in deep learning for IoT network intrusion detection.
  • Optimizing deep learning models through advanced algorithms like NSBPSO is crucial for bolstering IoT security.
  • The developed method provides a more accurate and performant solution for identifying network intrusions in IoT environments.