Identification of VoIP encrypted traffic using a machine learning approach

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@Article{Alshammari:2015:JKSUCIS,

• author = "Riyad Alshammari and A. Nur Zincir-Heywood",
• title = "Identification of {VoIP} encrypted traffic using a machine learning approach",
• journal = "Journal of King Saud University - Computer and Information Sciences",
• volume = "27",
• number = "1",
• pages = "77--92",
• year = "2015",
• keywords = "genetic algorithms, genetic programming, Machine learning, Encrypted traffic, Robustness, Network signatures",
• ISSN = "1319-1578",
• DOI = "doi:10.1016/j.jksuci.2014.03.013",
• URL = "http://www.sciencedirect.com/science/article/pii/S1319157814000561",
• abstract = "We investigate the performance of three different machine learning algorithms, namely C5.0, AdaBoost and Genetic programming (GP), to generate robust classifiers for identifying VoIP encrypted traffic. To this end, a novel approach (Alshammari and Zincir-Heywood, 2011) based on machine learning is employed to generate robust signatures for classifying VoIP encrypted traffic. We apply statistical calculation on network flows to extract a feature set without including payload information, and information based on the source and destination of ports number and IP addresses. Our results show that finding and employing the most suitable sampling and machine learning technique can improve the performance of classifying VoIP significantly.",

}

Genetic Programming entries for Riyad Alshammari Nur Zincir-Heywood

Citations