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Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments.

Abdulaziz Aldaej1, Tariq Ahamed Ahanger2, Imdad Ullah3

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a new Deep Learning (DL) intrusion detection system (IDS) for the Internet of Things (IoT). The DL-based IDS efficiently detects cyberattacks on edge devices, maintaining high accuracy with reduced data.

Keywords:
AIIDSIoTedge computingsecurity

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Intrusion Detection

Background:

  • The Internet of Things (IoT) generates massive data, posing challenges for cybersecurity.
  • Deep Learning (DL) shows promise for detecting cyberattacks in IoT environments.
  • Current Intrusion Detection Systems (IDS) struggle with the scale and computational demands of DL for edge deployment.

Purpose of the Study:

  • To propose an edge-cloud-based IoT IDS leveraging DL for efficient cyberattack detection.
  • To address the limitations of current IDS in handling large IoT data volumes and computational requirements.
  • To enable timely threat detection closer to IoT edge devices for critical infrastructure protection.

Main Methods:

  • Developed a distributed edge-cloud architecture for IoT intrusion detection.
  • Implemented attribute selection on time-series IoT data to reduce dataset size.
  • Trained a DL model using Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for attack detection.
  • Validated the model on the high-dimensional BoT-IoT dataset.

Main Results:

  • Attribute selection reduced dataset size by 85% without compromising detection capabilities.
  • The DL models achieved high performance metrics: 98.25% recall, 99.12% F1-score, 99.56% accuracy, and 99.45% precision.
  • Models trained on the reduced dataset showed no underfitting or overfitting.
  • The proposed solution demonstrated efficient scalability for large IoT data volumes.

Conclusions:

  • The proposed DL-based IoT IDS is effective and scalable for edge-cloud deployments.
  • It offers a viable solution for real-time cyberattack detection in IoT environments.
  • The approach successfully balances performance and computational efficiency for resource-constrained edge devices.