Identifying skype traffic in a large-scale flow data repository
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
High throughput and programmable online trafficclassifier on FPGA
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
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In this work, AdaBoost and C4.5, are employed for classifying Skype direct (UDP and TCP) communications from traffic log files. Pre-processing is applied to the traffic data to express it as flows, which is later converted into a descriptive feature set. The aforementioned algorithms are then evaluated on this feature set. Results show that a 98% detection rate with6% false positive rate for UDP based Skype and a 94% detection rate with 4% false positive rate for TCP based Skype is possible to achieve.