Intrusion detection by machine learning: A review

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  author =       "Chih-Fong Tsai and Yu-Feng Hsu and Chia-Ying Lin and 
                 Wei-Yang Lin",
  title =        "Intrusion detection by machine learning: A review",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "10",
  pages =        "11994--12000",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.05.029",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Intrusion
                 detection, Machine learning, Hybrid classifiers,
                 Ensemble classifiers",
  abstract =     "The popularity of using Internet contains some risks
                 of network attacks. Intrusion detection is one major
                 research problem in network security, whose aim is to
                 identify unusual access or attacks to secure internal
                 networks. In literature, intrusion detection systems
                 have been approached by various machine learning
                 techniques. However, there is no a review paper to
                 examine and understand the current status of using
                 machine learning techniques to solve the intrusion
                 detection problems. This chapter reviews 55 related
                 studies in the period between 2000 and 2007 focusing on
                 developing single, hybrid, and ensemble classifiers.
                 Related studies are compared by their classifier
                 design, datasets used, and other experimental setups.
                 Current achievements and limitations in developing
                 intrusion detection systems by machine learning are
                 present and discussed. A number of future research
                 directions are also provided.",
  notes =        "survey",

Genetic Programming entries for Chih-Fong Tsai Yu-Feng Hsu Chia-Ying Lin Wei-Yang Lin