An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets

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@InProceedings{Folino:2016:GECCOcomp,
  author =       "Gianluigi Folino and Francesco Sergio Pisani and 
                 Pietro Sabatino",
  title =        "An Incremental Ensemble Evolved by using Genetic
                 Programming to Efficiently Detect Drifts in Cyber
                 Security Datasets",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1103--1110",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, Colorado, USA",
  DOI =          "doi:10.1145/2908961.2931682",
  publisher =    "ACM",
  abstract =     "Unbalanced classes, the ability to detect changes in
                 real-time, the speed of the streams and other peculiar
                 characteristics make most of the data mining algorithms
                 not apt to operate with datasets in the cyber security
                 domain. To overcome these issues, we propose an
                 ensemble-based algorithm, using a distributed Genetic
                 Programming framework to generate the function to
                 combine the classifiers and efficient strategies to
                 react to changes in data. After that the base
                 classifiers are trained, the combining function of the
                 ensemble, based on non-trainable functions, can be
                 generated without any extra phase of training, while
                 the drift detection function adopted, together with a
                 strategy for replacing classifiers, permits to respond
                 in an efficient way to changes.

                 Preliminary experiments conducted on an artificial
                 dataset and on a real intrusion detection dataset show
                 the effectiveness of the approach.",
  publisher_address = "New York, NY, USA",
}

Genetic Programming entries for Gianluigi Folino Francesco Sergio Pisani Pietro Sabatino

Citations