Abstract
We propose a novel online monitoring approach to distinguish between attacks and normal activity in SIP-based Voice over IP environments. We demonstrate the efficiency of the approach even when only limited data sets are used in learning phase. The solution builds on the monitoring of a set of 38 features in VoIP flows and uses Support Vector Machines for classification. We validate our proposal through large offline experiments performed over a mix of real world traces from a large VoIP provider and attacks locally generated on our own testbed. Results show high accuracy of detecting SPIT and flooding attacks and promising performance for an online deployment are measured.
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Nassar, M., State, R., Festor, O. (2008). Monitoring SIP Traffic Using Support Vector Machines. In: Lippmann, R., Kirda, E., Trachtenberg, A. (eds) Recent Advances in Intrusion Detection. RAID 2008. Lecture Notes in Computer Science, vol 5230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87403-4_17
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DOI: https://doi.org/10.1007/978-3-540-87403-4_17
Publisher Name: Springer, Berlin, Heidelberg
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