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Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine

Muhammad Bisri Musthafa1, Samsul Huda2, Yuta Kodera1

  • 1Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan.

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|July 13, 2024
PubMed
Summary

Optimizing intrusion detection systems (IDS) for the Internet of Things (IoT) is crucial for cybersecurity. This study enhances IDS performance by using class balancing and feature selection, with LSTM stacking achieving superior accuracy in detecting network attacks.

Keywords:
class balancingensemble techniquefeature selectionintrusion detection systemstacked long short-term memory

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates robust intrusion detection systems (IDS) for enhanced cybersecurity.
  • Traditional IDS methods struggle with novel threats, highlighting the need for advanced techniques like machine learning (ML) and deep learning (DL).
  • ML and DL models in IDS face challenges such as overfitting and the impact of irrelevant features, compromising their effectiveness.

Purpose of the Study:

  • To optimize intrusion detection in IoT environments by addressing ML model limitations.
  • To improve the detection of novel and sophisticated network attacks.
  • To enhance the reliability and performance of IDS through effective preprocessing techniques.

Main Methods:

  • Implemented a preprocessing scheme involving class balancing and feature selection for optimizing ML models.
  • Evaluated two ensemble models: Support Vector Machine (SVM) with bagging and Long Short-Term Memory (LSTM) with stacking.
  • Utilized the UNSW-NB15 and NSL-KD datasets for experimental evaluation.

Main Results:

  • The LSTM stacking model with Analysis of Variance (ANOVA) feature selection demonstrated superior performance in classifying network attacks.
  • Achieved high accuracies of 96.92% and 99.77% on the UNSW-NB15 and NSL-KD datasets, respectively.
  • Reported minimal overfitting with values of 0.33% and 0.04%, and high Area Under the Curve (AUC) values of 0.9665 and 0.9971.

Conclusions:

  • The proposed optimization scheme, particularly LSTM stacking with ANOVA feature selection, significantly enhances IoT intrusion detection capabilities.
  • The model effectively mitigates overfitting and improves the detection of diverse network attacks.
  • This approach offers a more resilient and accurate cybersecurity solution for the expanding landscape of connected IoT devices.