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dc.contributor.authorVainer, Mark
dc.date.accessioned2023-09-18T16:39:35Z
dc.date.available2023-09-18T16:39:35Z
dc.date.issued2023
dc.identifier.other(crossref_id)146937306
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115565
dc.description.abstractThis article proposes a method for multi-purpose password dataset generation suitable for use in further machine learning and other research related, directly or indirectly, to passwords. Currently, password datasets are not suitable for machine learning or decision-driven password cracking. Most password datasets are just any old password dictionaries that contain only leaked and common passwords and no other information. Other password datasets are small and include only weak passwords that have previously been leaked. The literature is rich in terms of methods used for password cracking based on password datasets. Those methods are mainly focused on generating more password candidates like the ones included in the training dataset. The proposed method exploits statistical analysis of leaked passwords and randomness to ensure diversity in the dataset. An experiment with the generated dataset has shown significant improvement in time when performing dictionary attack but not when performing brute-force attack.eng
dc.formatPDF
dc.format.extentp. 1-18
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.source.urihttps://journals.vilniustech.lt/index.php/NTCS/article/view/17639/11669
dc.titleMulti-purpose password dataset generation and its application in decision making for password cracking through machine learning
dc.typeStraipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed source
dcterms.accessRightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references26
dc.type.pubtypeS4 - Straipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed publication
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldN 009 - Informatika / Computer science
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsIK0101 - Informacijos ir informacinių technologijų sauga / Information and Information Technologies Security
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enpasswords
dc.subject.enpassword cracking
dc.subject.enpassword dataset
dc.subject.enpassword strength
dc.subject.enmachine learning
dcterms.sourcetitleNew trends in computer sciences
dc.description.issueiss. 1
dc.description.volumevol. 1
dc.publisher.nameVilnius Gediminas Technical University
dc.publisher.cityVilnius
dc.identifier.doi146937306
dc.identifier.doi10.3846/ntcs.2023.17639
dc.identifier.elaba162530031


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