Genetic Approximations for the Failure-Free Security Games

Aleksandr Lenin*, Jan Willemson, Anton Charnamord

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    This paper deals with computational aspects of attack trees, more precisely, evaluating the expected adversarial utility in the failure-free game, where the adversary is allowed to re-run failed atomic attacks an unlimited number of times. It has been shown by Buldas and Lenin that exact evaluation of this utility is an NP-complete problem, so a computationally feasible approximation is needed. In this paper we consider a genetic approach for this challenge. Since genetic algorithms depend on a number of non-trivial parameters, we face a multi-objective optimization problem and we consider several heuristic criteria to solve it.
    Original languageEnglish
    Title of host publicationDecision and Game Theory for Security
    Subtitle of host publication6th International Conference, GameSec 2015 London, UK, November 4-5, 2015 Proceedings
    EditorsMHR Khouzani, Emmanouil Panaousis, George Theodorakopoulos
    Place of PublicationLondon
    PublisherSpringer
    Pages311-321
    Number of pages11
    ISBN (Electronic)978-3-319-25594-1
    ISBN (Print)978-3-319-25593-4
    DOIs
    Publication statusPublished - 2015
    Event6th International Conference on Decision and Game Theory for Security, GameSec 2015 - London, United Kingdom
    Duration: 4 Nov 20155 Nov 2015
    Conference number: 6

    Publication series

    NameLecture notes in computer science
    PublisherSpringer
    Volume9406
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference6th International Conference on Decision and Game Theory for Security, GameSec 2015
    Abbreviated titleGameSec
    CountryUnited Kingdom
    CityLondon
    Period4/11/155/11/15

    Keywords

    • EC Grant Agreement nr.: FP7/318003
    • EC Grant Agreement nr.: FP7/2007-2013
    • Genetic algorithm
    • Mutation rate
    • Crossover operator
    • Crossover rate
    • Attack trees

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