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A comparative analysis of using ensemble trees for botnet detection and classification in IoT.

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Tree-based machine learning algorithms show strong potential for detecting Internet of Things (IoT) botnet attacks. Random Forest (RF) achieved superior detection accuracy and computational performance in an empirical study.

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Area of Science:

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

Background:

  • IoT devices are vulnerable to botnet attacks due to limited resources and diversity.
  • Existing machine learning (ML) and deep learning (DL) methods face challenges in achieving high accuracy with reasonable computational cost for IoT security.
  • Botnet attacks pose significant security challenges to IoT ecosystems.

Purpose of the Study:

  • To analytically study the performance of tree-based machine learning algorithms for detecting botnet attacks in IoT.
  • To compare the detection capability and computational performance of various tree-based ML algorithms.

Main Methods:

  • Empirical study using a public botnet dataset specific to IoT environments.
  • Comparison of a basic decision tree algorithm with ensemble learning methods (bagging and boosting).
  • Evaluation of algorithms based on detection accuracy and computational performance.

Main Results:

  • Tree-based ML algorithms demonstrate significant potential for detecting network intrusions in IoT.
  • The Random Forest (RF) algorithm achieved the highest performance for multi-class classification with an accuracy rate of 0.999991.
  • RF also yielded the highest results across all other evaluated performance measures.

Conclusions:

  • Tree-based ML algorithms, particularly RF, are highly effective for IoT botnet detection.
  • RF offers a promising solution for enhancing IoT security with high accuracy and efficient computation.
  • Further research into tree-based ML can significantly improve trust and growth in IoT technology.