Title :
A Preliminary Investigation of Skype Traffic Classification Using a Minimalist Feature Set
Author :
Angevine, Duffy ; Zincir-Heywood, A. Nur
Author_Institution :
Dalhousie Univ., Halifax, NS
Abstract :
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.
Keywords :
Internet telephony; learning (artificial intelligence); peer-to-peer computing; telecommunication computing; telecommunication traffic; transport protocols; AdaBoost algorithm; C4.5 algorithm; Skype traffic classification; TCP communication; UDP communication; machine learning algorithm; minimalist feature set; peer-to-peer VoIP network; traffic log file; Availability; Cryptography; Hidden Markov models; Machine learning algorithms; Payloads; Privacy; Protocols; Telecommunication traffic; Traffic control; Tunneling; encrypted; skype; traffic classification;
Conference_Titel :
Availability, Reliability and Security, 2008. ARES 08. Third International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3102-1
DOI :
10.1109/ARES.2008.158
