Combining Ensemble of Classifiers by using Genetic Programming for Cyber Security Applications

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

@InProceedings{Folino:2015:evoApplications,
  author =       "Gianluigi Folino and Francesco Sergio Pisani",
  title =        "Combining Ensemble of Classifiers by using Genetic
                 Programming for Cyber Security Applications",
  booktitle =    "18th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2015",
  editor =       "Antonio M. Mora and Giovanni Squillero",
  series =       "LNCS",
  volume =       "9028",
  publisher =    "Springer",
  pages =        "54--66",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-16548-6",
  DOI =          "doi:10.1007/978-3-319-16549-3_5",
  abstract =     "Classification is a relevant task in the cyber
                 security domain, but it must be able to cope with
                 unbalanced and/or incomplete datasets and must also
                 react in real-time to changes in the data. Ensemble of
                 classifiers are a useful tool for classification in
                 hard domains as they combine different classifiers that
                 together provide complementary information. However,
                 most of the ensemble-based algorithms require an
                 extensive training phase and need to be re-trained in
                 case of changes in the data.

                 This work proposes a Genetic Programming-based
                 framework to generate a function for combining an
                 ensemble, having some interesting properties: the
                 models composing the ensemble are trained only on a
                 portion of the training set, and then, they can be
                 combined and used without any extra phase of training;
                 furthermore, in case of changes in the data, the
                 function can be recomputed in an incrementally way,
                 with a moderate computational effort.

                 Experiments conducted on unbalanced datasets and on a
                 well-known cyber-security dataset assess the goodness
                 of the approach.",
  notes =        "evoCOMNET EvoApplications2015 held in conjunction with
                 EuroGP'2015, EvoCOP2015 and EvoMusArt2015
                 http://www.evostar.org/2015/cfp_evoapps.php",
}

Genetic Programming entries for Gianluigi Folino Francesco Sergio Pisani

Citations