A Preliminary Investigation of Skype Traffic Classification Using a Minimalist Feature Set

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 with 6% 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.

• Type: Conference
• Year: 2008
• Where: IEEEARES
• Authors: Duffy Angevine, A. Nur Zincir-Heywood
• Keywords: Detection Rate, False Positive Rate, IEEEARES 2008, Security Privacy, Udp Based Skype