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GeNIS: A modular dataset for network intrusion detection and classification.

Miguel Silva1, Daniela Pinto1, João Vitorino1

  • 1Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal.

Data in Brief
|April 14, 2025
PubMed
Summary
This summary is machine-generated.

A new dataset, GeNIS, offers realistic cyberattack and normal network traffic data for small and medium-sized enterprises. This resource aids in developing better artificial intelligence-driven intrusion detection systems.

Keywords:
Anomaly detectionAttack classificationCybersecurityDatasetMachine learningNetwork flowPacket capture

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Area of Science:

  • Cybersecurity
  • Network Security
  • Data Science

Background:

  • High-quality data is crucial for AI-powered cyberattack detection.
  • A lack of labeled datasets hinders the development of intrusion detection systems (IDS) for small and medium-sized enterprises (SMEs).
  • Realistic network traffic data is needed to tailor IDS to specific organizational needs.

Purpose of the Study:

  • To introduce the GECAD Network Intrusion Scenarios (GeNIS) dataset.
  • To provide a benchmark dataset for training and evaluating AI models for cyberattack detection.
  • To support the improvement of intrusion detection systems for SMEs.

Main Methods:

  • Recorded realistic normal and attack network traffic on the Airbus CyberRange platform.
  • Generated labeled network flows from raw packet captures (PCAPNG).
  • Computed statistical features and created filtered CSV files with various flow intervals.

Main Results:

  • The GeNIS dataset includes over 37 million packets and 2.8 million flows.
  • Features represent diverse traffic patterns: attackers, normal users, administrators, and background traffic.
  • The dataset is preprocessed for machine learning and deep learning model training.

Conclusions:

  • GeNIS addresses the scarcity of labeled cyberattack datasets for SMEs.
  • The dataset enables in-depth analysis and the development of robust intrusion detection models.
  • It facilitates the creation of more effective and adaptable cybersecurity solutions.