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IoT-based intrusion detection system using convolution neural networks.

Abdullah Aljumah1

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia.

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|October 29, 2021
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Summary
This summary is machine-generated.

A new Temporal Convolution Neural Network (TCNN) offers advanced intrusion detection for the Internet of Things (IoT). This deep learning model achieves high accuracy in identifying network threats, outperforming traditional methods.

Keywords:
Internet of ThingsIntrusion Detection SystemLinear RegressionRandom Forest

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The proliferation of connected devices in the Information and Communication Technology (ICT) era generates vast data traffic, necessitating advanced methods for network anomaly detection.
  • Intrusion detection systems (IDS) are crucial for identifying unusual network loads and potential security threats in complex network environments.

Purpose of the Study:

  • To propose a deep learning-empowered intrusion detection system (IDS) for the Internet of Things (IoT) by identifying core design principles.
  • To introduce the Temporal Convolution Neural Network (TCNN) model for IoT-IDS, integrating Convolutional Neural Network (CNN) and generic convolution.

Main Methods:

  • The TCNN model incorporates the Synthetic Minority Oversampling Technique with Nominal Continuity (SMOTENC) to address imbalanced datasets.
  • Effective feature engineering techniques, including attribute transformation and reduction, are employed alongside TCNN.
  • The TCNN model's performance is benchmarked against traditional machine learning algorithms (Random Forest, Logistic Regression) and other deep learning models (LSTM, CNN) using the Bot-IoT dataset.

Main Results:

  • The TCNN model demonstrated a high multi-class traffic detection accuracy of 99.9986 percent.
  • TCNN exhibited a strong balance between efficacy and performance, outperforming other deep learning-based intrusion detection systems.
  • The training period for TCNN was comparable to that of CNN, indicating efficient computation.

Conclusions:

  • The TCNN model presents a highly effective and efficient solution for deep learning-empowered intrusion detection in IoT environments.
  • TCNN offers superior performance compared to existing deep learning IDS, particularly in handling imbalanced datasets and achieving high detection accuracy.
  • The proposed design principles and TCNN model contribute significantly to advancing cybersecurity in the age of massive data traffic from connected objects.