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Data set and machine learning models for the classification of network traffic originators.

Daniele Canavese1, Leonardo Regano1, Cataldo Basile1

  • 1Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino 10129, Italy.

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Summary
This summary is machine-generated.

This study introduces a dataset and machine learning models for classifying encrypted network traffic. The models analyze TCP headers to identify tools like web crawlers and browsers, aiding network security analysis.

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DoS attacksIntrusion detectionMachine learningNetwork traffic anomalyWeb crawling

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Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Increasing encryption in network traffic hinders security analysis.
  • Traditional traffic analysis methods struggle with encrypted data.

Purpose of the Study:

  • To present a dataset of network statistics for encrypted traffic.
  • To describe machine learning models for classifying network tools based on TCP headers.
  • To facilitate the development of new network traffic analysis tools.

Main Methods:

  • Collected network statistics from TCP flows generated by various tools (stress, crawling, browsing).
  • Developed and trained machine learning models using the collected dataset.
  • Focused analysis on TCP headers to ensure compatibility with encrypted payloads.

Main Results:

  • Successfully trained models to classify traffic by tool category, specific tool, and tool version.
  • Demonstrated the effectiveness of TCP header statistics for classifying encrypted traffic.
  • Created a valuable dataset for training and evaluating network tool classification models.

Conclusions:

  • Machine learning models analyzing TCP header statistics can effectively classify encrypted network traffic.
  • The presented dataset and models offer a robust solution for network security and tool identification.
  • This work supports the advancement of network security analysis in the age of widespread encryption.