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Automatically identifying the function and intent of posts in underground forums.

Andrew Caines1, Sergio Pastrana2, Alice Hutchings2

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Area of Science:

  • Computer Science
  • Social Science
  • Cybersecurity

Background:

  • Understanding user behavior in online hacking forums is crucial for analyzing cybercrime.
  • Automatic classification of forum posts can provide valuable insights into user interactions and motivations.

Purpose of the Study:

  • To design an annotation schema for labeling hacking forum posts by post type, author intent, and addressee.
  • To develop and evaluate rule-based and machine learning models for automatic classification of these properties.
  • To explore applications in analyzing forum conversations and identifying key social network actors.

Main Methods:

  • Manual annotation of forum posts for post type, author intent, and addressee.
  • Training and comparison of rule-based (logical) and machine learning (statistical) classification models.
  • Development of a hybrid logical-statistical model for improved classification accuracy.

Main Results:

  • Substantial agreement was achieved for manual annotation of post type and addressee, with fair agreement for author intent.
  • A hybrid logical-statistical model demonstrated superior performance for predicting post type and author intent.
  • A purely statistical model was most effective for classifying the addressee of posts.

Conclusions:

  • Automatic classification of hacking forum posts is feasible and valuable for understanding user behavior in cybercrime-related social networks.
  • Hybrid and statistical models offer effective methods for analyzing online forum data.
  • The developed classification schema and models can support the analysis of online discussions and social network structures.