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Area of Science:

  • Computer Science
  • Network Security
  • Data Science

Background:

  • Large campus networks generate substantial web server traffic data.
  • Monitoring network traffic and host events is crucial for security and performance.
  • Existing datasets may lack comprehensive, correlated network and host-level information.

Purpose of the Study:

  • To present a novel, anonymized dataset of web server monitoring data.
  • To facilitate research in machine learning for network anomaly detection.
  • To enable the study of relationships between network traffic and server events.

Main Methods:

  • Collected seven days of network packet traces (HTTP over TLS 1.2) from eight web servers.
  • Captured host-based event logs from Internet Information Services (IIS) on the same servers.
  • Anonymized all data to protect privacy while retaining analytical value.
  • Provided tools and a guide for converting packet traces to IP flows.

Main Results:

  • A comprehensive dataset integrating network and host-based monitoring data was created.
  • The dataset includes encrypted HTTP traffic and detailed IIS logs with custom features.
  • Data anonymization was performed to ensure privacy preservation.
  • Tools for IP flow conversion were developed to aid network traffic analysis.

Conclusions:

  • The presented dataset is valuable for training machine learning models for anomaly detection.
  • It supports research into the correlation between network behavior and web server activity.
  • The dataset and accompanying tools enhance capabilities for network security and performance analysis.