Investigating Coevolutionary Archive Based Genetic Algorithms on Cyber Defense Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Garcia:2017:GECCO,
  author =       "Dennis Garcia and Anthony Erb Lugo and 
                 Erik Hemberg and Una-May O'Reilly",
  title =        "Investigating Coevolutionary Archive Based Genetic
                 Algorithms on Cyber Defense Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  month =        "15-19 " # jul,
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1455--1462",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, coevolution,
                 cybersecurity, evolutionary algorithms, genetic
                 algorithms, network",
  URL =          "http://doi.acm.org/10.1145/3067695.3076081",
  DOI =          "doi:10.1145/3067695.3076081",
  acmid =        "3076081",
  size =         "8 pages",
  abstract =     "We introduce a new cybersecurity project named RIVALS.
                 RIVALS will assist in developing network defence
                 strategies through modelling adversarial network attack
                 and defense dynamics. RIVALS will focus on peer-to-peer
                 networks and use coevolutionary algorithms. In this
                 contribution, we describe RIVALS' current suite of
                 coevolutionary algorithms that use archiving to
                 maintain progressive exploration and that support
                 different solution concepts as fitness metrics. We
                 compare and contrast their effectiveness by executing a
                 standard coevolutionary benchmark (Compare-on-one) and
                 RIVALS simulations on 3 different network topologies.
                 Currently, we model denial of service (DOS) attack
                 strategies by the attacker selecting one or more
                 network servers to disable for some duration. Defenders
                 can choose one of three different network routing
                 protocols: shortest path, flooding and a peer-to-peer
                 ring overlay to try to maintain their performance.
                 Attack completion and resource cost minimization serve
                 as attacker objectives. Mission completion and resource
                 cost minimization are the reciprocal defender
                 objectives. Our experiments show that existing
                 algorithms either sacrifice execution speed or forgo
                 the assurance of consistent results. rIPCA, our
                 adaptation of a known coevolutionary algorithm named
                 IPC A, is able to more consistently produce high
                 quality results, albeit without IPCA's guarantees for
                 results with monotonically increasing performance,
                 without sacrificing speed.",
  notes =        "See also
                 https://dspace.mit.edu/handle/1721.1/112841

                 Also known as \cite{Garcia:2017:ICA:3067695.3076081}
                 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 Dennis Garcia Anthony Erb Lugo Erik Hemberg Una-May O'Reilly

Citations