An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype

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@InProceedings{Alshammari:2010:CNSM,

• author = "Riyad Alshammari and A. Nur Zincir-Heywood",
• title = "An investigation on the identification of {VoIP} traffic: Case study on Gtalk and Skype",
• booktitle = "2010 International Conference on Network and Service Management (CNSM)",
• year = "2010",
• month = "25-29 " # oct,
• pages = "310--313",
• abstract = "The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours, in effect providing further insight into the problem domain. Thus, the objective of this work is to classify VoIP encrypted traffic, where Gtalk and Skype applications are taken as good representatives. To this end, three different machine learning based approaches, namely, C4.5, AdaBoost and Genetic Programming (GP), are evaluated under data sets common and independent from the training condition. In this case, flow based features are employed without using the IP addresses, source/destination ports and payload information. Results indicate that C4.5 based machine learning approach has the best performance.",
• keywords = "genetic algorithms, genetic programming, AdaBoost, C4.5, Gtalk, IP address, Skype, VoIP encrypted traffic, machine learning, source/destination port, Internet telephony, learning (artificial intelligence), telecommunication traffic",
• DOI = "doi:10.1109/CNSM.2010.5691210",
• notes = "Also known as \cite{5691210}",

}

Genetic Programming entries for Riyad Alshammari Nur Zincir-Heywood

Citations