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SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks.

Segun I Popoola1, Bamidele Adebisi1, Ruth Ande1

  • 1Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
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This summary is machine-generated.

This study introduces a Deep Recurrent Neural Network (DRNN) model enhanced with Synthetic Minority Oversampling Technique (SMOTE) for effective botnet detection in Internet of Things (IoT) networks, even with imbalanced data.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Botnets pose a significant threat to Internet of Things (IoT) security by exploiting distributed computing resources.
  • Existing Machine Learning (ML) and Deep Learning (DL) models struggle with imbalanced network traffic data, leading to poor detection of botnet attacks.
  • Class imbalance in training datasets negatively impacts the performance of botnet detection models, particularly for minority attack classes.

Purpose of the Study:

  • To propose an efficient Deep Learning (DL) algorithm for detecting botnet attacks in IoT networks that effectively handles highly imbalanced network traffic data.
  • To improve the classification performance of botnet detection models by addressing the challenge of imbalanced datasets.

Main Methods:

  • Developed a Deep Recurrent Neural Network (DRNN) model for learning hierarchical features from network traffic data.
Keywords:
Internet of Thingsbotnetcybersecuritydeep learningintrusion detection

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  • Implemented the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for minority classes, balancing the dataset.
  • Evaluated the performance of the standard DRNN model and the proposed SMOTE-DRNN model using the Bot-IoT dataset.

    • Main Results:

    • The standard DRNN model showed degraded performance metrics (precision, recall, F1 score, AUC, GM, MCC) when trained on imbalanced data.
    • The SMOTE-DRNN model achieved superior classification performance, reaching 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM, and 99.62% MCC.
    • The SMOTE-DRNN model demonstrated better performance compared to existing state-of-the-art ML and DL models.

    Conclusions:

    • Class imbalance in network traffic data significantly hinders the effectiveness of DL-based botnet detection in IoT environments.
    • The proposed SMOTE-DRNN algorithm effectively mitigates the negative impact of data imbalance, leading to highly accurate botnet attack detection.
    • The SMOTE-DRNN model offers a robust and efficient solution for enhancing the security of IoT networks against botnet cyberattacks.