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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorIturbe, Mikel
dc.contributor.otherEceiza Olaizola, Maialen
dc.contributor.otherFlores, José Luis
dc.date.accessioned2022-11-17T14:16:30Z
dc.date.available2022-11-17T14:16:30Z
dc.date.issued2023
dc.identifier.issn0167-4048en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=169706en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5858
dc.description.abstractFuzzing is nowadays one of the most widely used bug hunting techniques. By automatically generating malformed inputs, fuzzing aims to trigger unwanted behavior on its target. While fuzzing research has matured considerably in the last years, the evaluation and comparison of different fuzzing proposals remain challenging, as no standard set of metrics, data, or experimental conditions exist to allow such observation. This paper aims to fill that gap by proposing a standard set of features to allow such comparison. For that end, it first reviews the existing evaluation methods in the literature and discusses all existing metrics by evaluating seven fuzzers under identical experimental conditions. After examining the obtained results, it recommends a set of practices –particularly on the metrics to be used–, to allow proper comparison between different fuzzing proposals.en
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2022 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectfuzzingen
dc.subjectEvaluation methodologyen
dc.subjectsecurityen
dc.subjectsoftware testingen
dc.subjectMetricsen
dc.titleImproving fuzzing assessment methods through the analysis of metrics and experimental conditionsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceComputers & Securityen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.cose.2022.102946en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Elkartek 2021/KK-2021-00091/CAPV/REal tiME control and embeddeD securitY/REMEDYen
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2025/IT1676-22/CAPV/Grupo de sistemas inteligentes para sistemas industriales/en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Programa Bikaintek 2019/20-AF-W2-2019-00006/CAPV//en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/No 101021911/EU/A Cognitive Detection System for Cybersecure Operational/IDUNNen
local.contributor.otherinstitutionhttps://ror.org/03hp1m080es
local.source.detailsVol. 124. Article 102946. January, 2023en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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