Identification of VoIP encrypted traffic using a machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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