Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Zhou:2024:evoapplications,

• author = "Zhilei Zhou and Nur Zincir-Heywood and Malcolm I. Heywood",
• title = "Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks",
• booktitle = "27th International Conference, EvoApplications 2024",
• year = "2024",
• editor = "Stephen Smith and Joao Correia and Christian Cintrano",
• series = "LNCS",
• volume = "14634",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• pages = "361--376",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Boosting, Bagging, Stacking, Evolutionary Ensemble Learning, Intrusion Detection",
• isbn13 = "978-3-031-56851-0",
• URL = "https://rdcu.be/dDZWe",
• DOI = "doi:10.1007/978-3-031-56852-7_23",
• 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.",
• 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