One-class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Cao:2016:EuroGP,

• author = "Van Loi Cao and Miguel Nicolau and James McDermott",
• title = "One-class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "3--18",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, Anomaly detection, One-class classification, Kernel Density Estimation",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_1",
• abstract = "A novel approach is proposed for fast anomaly detection by one-class classification. Standard kernel density estimation is first used to obtain an estimate of the input probability density function, based on the one-class input data. This can be used for anomaly detection: query points are classed as anomalies if their density is below some threshold. The disadvantage is that kernel density estimation is lazy, that is the bulk of the computation is performed at query time. For large datasets it can be slow. Therefore it is proposed to approximate the density function using genetic programming symbolic regression, before imposing the threshold. The runtime of the resulting genetic programming trees does not depend on the size of the training data. The method is tested on datasets including in the domain of network security. Results show that the genetic programming approximation is generally very good, and hence classification accuracy approaches or equals that when using kernel density estimation to carry out one-class classification directly. Results are also generally superior to another standard approach, one-class support vector machines.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Van Loi Cao Miguel Nicolau James McDermott

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