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Behavioral Intruder Detection Based on Browsing Patterns with Automated Grouping of Requested Webpages.

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Online fraud is rising, but behavioral impersonation detection using web server logs can identify attackers. Our Siamese neural network achieves 90% accuracy in detecting fraudulent sessions, offering a scalable solution for online services.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Online fraud and impersonation attacks pose significant threats to digital services, particularly in the banking sector.
  • Current authentication methods often lack robust behavioral analysis, relying heavily on traditional factors or limited mouse/keyboard dynamics.

Purpose of the Study:

  • To investigate the efficacy of using standard web-server logs for behavioral impersonation detection in online banking.
  • To develop and evaluate a scalable machine learning approach for identifying fraudulent user sessions.

Main Methods:

  • Extraction and analysis of behavioral patterns from web server logs.
  • Implementation of a Siamese neural network for classifying user web sessions.
  • Development of an automated procedure using low-rank approximation for grouping web pages to handle variability.

Main Results:

  • The Siamese neural network achieved 90% accuracy in classifying user sessions.
  • The automated page grouping procedure improved classification accuracy and reduced manual data analysis.
  • The methods demonstrated applicability in a banking scenario using real-world weblogs.

Conclusions:

  • Behavioral patterns from web server logs can be effectively utilized for fraud detection in online services.
  • The proposed automated approach offers a scalable and viable solution for combating behavioral impersonation, enhancing security in online banking.