One-Class Classification of Low Volume DoS Attacks with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Picek:2017:GPTP,

• author = "Stjepan Picek and Erik Hemberg and Domagoj Jakobovic and Una-May O'Reilly",
• title = "One-Class Classification of Low Volume {DoS} Attacks with Genetic Programming",
• booktitle = "Genetic Programming Theory and Practice XV",
• editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo",
• year = "2017",
• series = "Genetic and Evolutionary Computation",
• pages = "149--168",
• address = "University of Michigan in Ann Arbor, USA",
• month = may # " 18--20",
• organisation = "the Center for the Study of Complex Systems",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-90511-2",
• URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_10",
• DOI = "10.1007/978-3-319-90512-9_10",
• abstract = "We use Genetic Programming in a machine learning approach to learn a detector of DoS-related network intrusion events. We present a one class classifier technique that trains a model from one class of data-normal, i.e., non-intrusion events. Our technique, after ensemble fusion, is competitive with one-class modelling with Support Vector Machines. We compare with three datasets and our best GP-based classifiers are able to outperform one-class SVM. For two out of four test cases, the advantage of GP classifiers when compared with one-class SVM is less than 1% which does not represent a significant improvement. On the last two cases, GP achieves significantly better results and making it a viable choice for anomaly detection task.",
• notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018",

}

Genetic Programming entries for Stjepan Picek Erik Hemberg Domagoj Jakobovic Una-May O'Reilly

Citations