An ensemble-based evolutionary framework for coping with distributed intrusion detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Folino:2010:GPEM,
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author = "Gianluigi Folino and Clara Pizzuti and
Giandomenico Spezzano",
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title = "An ensemble-based evolutionary framework for coping
with distributed intrusion detection",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2010",
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volume = "11",
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number = "2",
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pages = "131--146",
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month = jun,
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note = "Special issue on parallel and distributed evolutionary
algorithms, part II",
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keywords = "genetic algorithms, genetic programming, Intrusion
detection, Ensemble classifiers, Distributed
evolutionary algorithms",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-010-9101-6",
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size = "16 pages",
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abstract = "A distributed data mining algorithm to improve the
detection accuracy when classifying malicious or
unauthorized network activity is presented. The
algorithm is based on genetic programming (GP) extended
with the ensemble paradigm. GP ensemble is particularly
suitable for distributed intrusion detection because it
allows to build a network profile by combining
different classifiers that together provide
complementary information. The main novelty of the
algorithm is that data is distributed across multiple
autonomous sites and the learner component acquires
useful knowledge from this data in a cooperative way.
The network profile is then used to predict abnormal
behavior. Experiments on the KDD Cup 1999 Data show the
capability of genetic programming in successfully
dealing with the problem of intrusion detection on
distributed data.",
- }
Genetic Programming entries for
Gianluigi Folino
Clara Pizzuti
Giandomenico Spezzano
Citations