Developing Proactive Defenses for Computer Networks with Coevolutionary Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{Lugo:2017:GECCO,
  author =       "Anthony Erb Lugo and Dennis Garcia and 
                 Erik Hemberg and Una-May O'Reilly",
  title =        "Developing Proactive Defenses for Computer Networks
                 with Coevolutionary Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "273--274",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, cybersecurity
                 coevolution, evolutionary algorithms, network",
  URL =          "http://doi.acm.org/10.1145/3067695.3089234",
  DOI =          "doi:10.1145/3067695.3089234",
  acmid =        "3089234",
  size =         "2 pages",
  abstract =     "Our cybersecurity tool, RIVALS, develops adaptive
                 network defence strategies by modelling adversarial
                 network attack and defense behaviour in peer-to-peer
                 networks via coevolutionary algorithms. Currently
                 RIVALS DOS attacks are modestly modeled by the
                 selection of a node that is completely disabled for a
                 resource-limited duration. Defenders have three
                 different network routing protocols. Attack or mission
                 completion and resource cost metrics serve as attacker
                 and defender objectives. This work also includes a
                 description of RIVALS suite of coevolutionary
                 algorithms that explore archiving as a means of
                 maintaining progressive exploration and support the
                 evaluation of different solution concepts. To compare
                 and contrast the effectiveness of each algorithm, we
                 execute simulations on 3 different network topologies.
                 Our experiments show that it is possible to forgo the
                 assurance of monotonically increasing results and still
                 retain high quality results.",
  notes =        "Also known as \cite{Lugo:2017:DPD:3067695.3089234}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Anthony Erb Lugo Dennis Garcia Erik Hemberg Una-May O'Reilly

Citations