Monitoring SIP Tra c Using Support Vector Machines
نویسندگان
چکیده
We propose a novel online monitoring approach to distinguish between attacks and normal activity in SIP-based Voice over IP environments. We demonstrate the e ciency 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 ows and uses Support Vector Machines for classi cation. We validate our proposal through large o ine 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 ooding attacks and promising performance for an online deployment are measured.
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