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Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.

Shaza Dawood Ahmed Rihan1, Mohammed Anbar2, Basim Ahmad Alabsi1

  • 1Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a meta-learning approach for identifying Internet of Things (IoT) network attacks. The method enhances security by combining deep learning models, with XGBoost achieving 98.75% accuracy.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) devices expands the attack surface for cyber threats.
  • High data volumes from IoT devices can overwhelm traditional security systems, hindering effective threat detection.
  • Existing security measures struggle to cope with the scale and complexity of IoT network vulnerabilities.

Purpose of the Study:

  • To propose a novel meta-learning framework for enhanced attack identification in IoT networks.
  • To address the security challenges posed by the increasing number of interconnected IoT devices and data overload.
  • To evaluate the efficacy of a meta-learner model integrating multiple deep learning and machine learning algorithms.

Main Methods:

  • Developed a meta-learner by stacking predictions from Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models.
Keywords:
Internet of ThingsInternet of Things attacksdeep-learning modelsmeta-learning approach

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  • Employed Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for meta-learner identification.
  • Conducted extensive evaluations using the 2020 IoT dataset to assess model performance.

    • Main Results:

    • The Extreme Gradient Boosting (XGBoost) model achieved the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%).
    • The Support Vector Machine (SVM) model demonstrated the highest recall (98.90%), showing a marginal improvement over XGBoost.
    • The meta-learning approach effectively enhanced attack detection capabilities in IoT environments.

    Conclusions:

    • The proposed meta-learning framework offers a robust solution for identifying attacks in complex IoT networks.
    • XGBoost and SVM models show significant promise for real-time threat detection and mitigation in IoT security.
    • This approach provides a scalable and effective strategy to bolster the security posture of interconnected IoT ecosystems.