Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Zhou:2024:evoapplications,
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author = "Zhilei Zhou and Nur Zincir-Heywood and
Malcolm I. Heywood",
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title = "Simple Efficient Evolutionary Ensemble Learning on
Network Intrusion Detection Benchmarks",
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booktitle = "27th International Conference, EvoApplications 2024",
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year = "2024",
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editor = "Stephen Smith and Joao Correia and
Christian Cintrano",
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series = "LNCS",
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volume = "14634",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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pages = "361--376",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Boosting,
Bagging, Stacking, Evolutionary Ensemble Learning,
Intrusion Detection",
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isbn13 = "978-3-031-56851-0",
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URL = "https://rdcu.be/dDZWe",
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DOI = "doi:10.1007/978-3-031-56852-7_23",
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abstract = "Training and deploying genetic programming (GP)
classifiers for intrusion detection tasks on the one
hand remains a challenge (high cardinality and high
class imbalance). On the other hand, GP solutions can
also be particularly lightweight from a deployment
perspective, enabling detectors to be deployed at the
edge without specialised hardware support. We compare
state-of-the-art ensemble learning solutions from GP
and XGBoost on three examples of intrusion detection
tasks with 250000 to 700000 training records, 8 to 115
features and 2 to 23 classes. XGBoost provides the most
accurate solutions, but at two orders of magnitude
higher complexity. Training time for the preferred GP
ensemble is in the order of minutes, but the
combination of simplicity and specificity is such that
the resulting solutions are more informative and
discriminatory. Thus, as the number of features
increases and/or classes increase, the resulting
ensembles are composed from particularly simple trees
that associate specific features with specific
behaviours.",
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notes = "http://www.evostar.org/2024/ EvoApplications2024 held
in conjunction with EuroGP'2024, EvoCOP2024 and
EvoMusArt2024",
- }
Genetic Programming entries for
Zhilei Zhou
Nur Zincir-Heywood
Malcolm Heywood
Citations