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Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. <i>Sensors</i> 2022, <i>22</i>, 7409.

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BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning.

Ogobuchi Daniel Okey1, Siti Sarah Maidin2, Pablo Adasme3

  • 1Department of Systems Engineering and Automation, Federal University of Lavras, Lavras 37203-202, MG, Brazil.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces BoostedEnML, an efficient intrusion detection system for Internet of Things (IoT) security. The model achieves 100% accuracy in detecting multiple cyberattacks, enhancing IoT network defense.

Keywords:
BoostedEnMLInternet of ThingsSMOTEcyberattacksdata imbalanceensemble algorithmsmachine learning IDS

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Intrusion Detection

Background:

  • Internet of Things (IoT) systems face increasing security threats, necessitating robust intrusion detection systems (IDS).
  • Existing machine learning-based IDS often struggle with the dynamic nature of IoT networks and require improved detection rates and lower computational overhead.
  • Ensemble methods offer a promising approach to enhance IDS performance by combining multiple machine learning classifiers.

Purpose of the Study:

  • To propose an efficient and accurate intrusion detection system (IDS) for Internet of Things (IoT) environments.
  • To develop a novel ensemble model, BoostedEnML, leveraging boosted machine learning classifiers for enhanced cyberattack detection.
  • To address the challenge of detecting multiple, sophisticated attacks in IoT networks with high precision and efficiency.

Main Methods:

  • Trained six distinct machine learning classifiers (DT, RF, ET, LGBM, AD, XGB) and created ensembles using stacking and majority voting.
  • Employed data balancing techniques including Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) sampling.
  • Constructed the BoostedEnML model using LightGBM and XGBoost, validated on two datasets containing diverse high-profile attacks (e.g., DDoS, DoS, botnets).

Main Results:

  • The proposed BoostedEnML model demonstrated superior performance compared to existing ensemble models.
  • Achieved 100% accuracy, precision, recall, F-score, and Area Under the Curve (AUC) for multiclass classification on the selected datasets.
  • The combination of LightGBM and XGBoost resulted in a lightweight yet highly efficient intrusion detection model.

Conclusions:

  • BoostedEnML offers a highly effective solution for detecting a wide range of cyberattacks in IoT networks.
  • The model's high performance metrics indicate its potential for real-world application in securing IoT ecosystems.
  • The research highlights the efficacy of boosted ensemble methods in creating efficient and accurate intrusion detection systems for resource-constrained IoT environments.