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Updated: Sep 30, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A Novel Framework for Generating Personalized Network Datasets for NIDS Based on Traffic Aggregation.

Pablo Velarde-Alvarado1, Hugo Gonzalez2, Rafael Martínez-Peláez3

  • 1Unidad Académica de Ciencias Básicas e Ingenierías, Universidad Autónoma de Nayarit, Tepic 63000, Mexico.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

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This summary is machine-generated.

Dataset scarcity for network intrusion detection is addressed by a novel framework generating realistic traffic data. This method, using real network traces, effectively creates datasets for training machine learning models, achieving high detection accuracy.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Data Science

Background:

  • Dataset scarcity is a significant challenge in developing effective network intrusion detection systems (NIDS).
  • Existing datasets may not accurately represent the complexity and evolving nature of modern network traffic, particularly botnet activities.

Purpose of the Study:

  • To address the challenge of limited datasets for network intrusion detection.
  • To develop a comprehensive framework for generating realistic network traffic datasets from aggregated real-world network traces.
  • To create tools for efficient attribute extraction and labeling of traffic sessions.

Main Methods:

  • Developed a framework integrating real network traces for dataset generation.
  • Implemented tools for attribute extraction and session labeling.
Keywords:
botnet detectionintrusion detectionmachine learningnetwork securitytraffic generationunbalanced dataset

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  • Generated a new dataset specifically containing botnet network traffic.
  • Utilized machine learning algorithms designed for imbalanced data to evaluate the generated dataset.

    • Main Results:

    • The framework successfully generated a new network traffic dataset.
    • Machine learning models trained on the generated dataset demonstrated strong performance.
    • Achieved high macro-average F1-score (0.97) and Matthews Correlation Coefficient (0.94) for botnet detection.

    Conclusions:

    • The proposed framework effectively overcomes dataset scarcity for network intrusion detection.
    • The generated dataset is suitable for training and evaluating machine learning models, especially for imbalanced data scenarios.
    • The framework and generated dataset show significant promise for improving cybersecurity defenses against botnets.