- 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)",
