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.
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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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Subtil, A., Oliveira, M.R., Valadas, R., Pacheco, A., Salvador, P. (2020). Detecting Internet-Scale Traffic Redirection Attacks Using Latent Class Models. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_37
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DOI: https://doi.org/10.1007/978-3-030-17065-3_37
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