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Ensemble learning-based IDS for sensors telemetry data in IoT networks.

Naila Naz1, Muazzam A Khan1, Suliman A Alsuhibany2

  • 1Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.

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|August 29, 2022
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Summary
This summary is machine-generated.

This study introduces a stacking-based ensemble model for intelligent intrusion detection in Internet of Things (IoT) networks. The model enhances security by effectively identifying unusual behavior and protecting IoT telemetry data from cyber threats.

Keywords:
IoTToN-IoTbaggingensemble learningintrusion detectionsensors security

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • The Internet of Things (IoT) connects smart devices, enabling data collection and communication via protocols vulnerable to attacks.
  • Interdependent IoT devices pose security risks, as compromised devices can manipulate telemetry data to control others.
  • Securing IoT networks against sophisticated threats is a significant and growing concern.

Purpose of the Study:

  • To develop an intelligent intrusion detection system (IDS) for enhanced IoT network security.
  • To propose a novel stacking-based ensemble model for detecting anomalous behavior in IoT devices.
  • To improve the accuracy and effectiveness of intrusion detection in IoT environments.

Main Methods:

  • Utilized a stacking-based ensemble machine learning model for intrusion detection.
  • Employed the TON-IoT (2020) dataset for model training and evaluation.
  • Assessed model performance in both binary and multi-class classification scenarios.

Main Results:

  • The proposed stacking-based ensemble model demonstrated significant improvements in accuracy.
  • The model outperformed traditional machine learning algorithms and other ensemble techniques.
  • Effective detection of unusual behavior was achieved across various sensors in the dataset.

Conclusions:

  • Stacking-based ensemble models offer a promising approach to enhance IoT security.
  • The developed model provides a more intelligent and robust solution for IoT intrusion detection.
  • Further research can explore advanced ensemble techniques for comprehensive IoT network protection.