A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers

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@InProceedings{Folino:2016:EvoApps,
  author =       "Gianluigi Folino and Francesco Sergio Pisani and 
                 Pietro Sabatino",
  title =        "A Distributed Intrusion Detection Framework Based on
                 Evolved Specialized Ensembles of Classifiers",
  booktitle =    "EvoApplications 2016",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9597",
  series =       "LNCS",
  pages =        "315--331",
  address =      "Porto, Portugal",
  month =        mar # " 30-" # apr # " 1",
  organisation = "Species",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-31204-0",
  DOI =          "doi:10.1007/978-3-319-31204-0_21",
  abstract =     "Modern intrusion detection systems must handle many
                 complicated issues in real-time, as they have to cope
                 with a real data stream; indeed, for the task of
                 classification, typically the classes are unbalanced
                 and, in addition, they have to cope with distributed
                 attacks and they have to quickly react to changes in
                 the data. Data mining techniques and, in particular,
                 ensemble of classifiers permit to combine different
                 classifiers that together provide complementary
                 information and can be built in an incremental way.
                 This paper introduces the architecture of a distributed
                 intrusion detection framework and in particular, the
                 detector module based on a meta-ensemble, which is used
                 to cope with the problem of detecting intrusions, in
                 which typically the number of attacks is minor than the
                 number of normal connections. To this aim, we explore
                 the usage of ensembles specialized to detect particular
                 types of attack or normal connections, and Genetic
                 Programming is adopted to generate a non-tra",
}

Genetic Programming entries for Gianluigi Folino Francesco Sergio Pisani Pietro Sabatino

Citations