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Detecting Internet-Scale Traffic Redirection Attacks Using Latent Class Models

  • Ana SubtilEmail author
  • M. Rosário Oliveira
  • Rui Valadas
  • Antonio Pacheco
  • Paulo Salvador
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

Abstract

Traffic redirection attacks based on BGP route hijacking has been an increasing concern in Internet security worldwide. This paper addresses the statistical detection of traffic redirection attacks based on the RTT data collected by a network of probes spread all around the world. Specifically, we use a Latent Class Model to combine the decisions of individual probes on whether an Internet site is being attacked, and use supervised learning methods to perform the probe decisions. We evaluate the methods in a large number of scenarios, and compare them with an empirically adjusted heuristic. Our method achieves very good performance, superior to the heuristic one. Moreover, we provide a comprehensive analysis of the merits of the Latent Class Model approach.

Keywords

Traffic redirection attack BGP security Statistical learning Latent Class Model 

Notes

Acknowledgments

This research was supported by Instituto de Telecomunicações, Centro de Matemática Computacional e Estocástica, and Fundação Nacional para a Ciência e Tecnologia, through projects PTDC/EEI-TEL/5708/2014, UID/EEA/50008/2013, and UID/Multi/04621/2013. A. Subtil was funded by the FCT grant SFRH/BD/69793/2010.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ana Subtil
    • 1
    Email author
  • M. Rosário Oliveira
    • 2
  • Rui Valadas
    • 1
  • Antonio Pacheco
    • 1
  • Paulo Salvador
    • 3
  1. 1.Instituto de Telecomunicações and Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.CEMAT and Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  3. 3.Instituto de Telecomunicações and DETIUniversidade de AveiroAveiroPortugal

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