dc.contributor.author | Darbutaitė, Ema | |
dc.contributor.author | Stefanovič, Pavel | |
dc.contributor.author | Ramanauskaitė, Simona | |
dc.date.accessioned | 2023-09-18T16:39:33Z | |
dc.date.available | 2023-09-18T16:39:33Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/115550 | |
dc.description.abstract | In an information-security-assurance system, humans are usually the weakest link. It is partly related to insufficient cybersecurity knowledge and the ignorance of standard security recommendations. Consequently, the required password-strength requirements in information systems are the minimum of what can be done to ensure system security. Therefore, it is important to use up-to-date and context-sensitive password-strength-estimation systems. However, minor languages are ignored, and password strength is usually estimated using English-only dictionaries. To change the situation, a machine learning approach was proposed in this article to support a more realistic model to estimate the strength of Lithuanian user passwords. A newly compiled dataset of password strength was produced. It integrated both international- and Lithuanian-language-specific passwords, including 6 commonly used password features and 36 similarity metrics for each item (4 similarity metrics for 9 different dictionaries). The proposed solution predicts the password strength of five classes with 77% accuracy. Taking into account the complexity of the accuracy of the Lithuanian language, the achieved result is adequate, as the availability of intelligent Lithuanian-language-specific password-cracking tools is not widely available yet. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-16 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | Agris | |
dc.source.uri | https://www.mdpi.com/2076-3417/13/13/7811 | |
dc.title | Machine-learning-based password-strength-estimation approach for passwords of Lithuanian context | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 33 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.researchfield | N 009 - Informatika / Computer science | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
dc.subject.en | password-strength estimation | |
dc.subject.en | machine learning | |
dc.subject.en | Lithuanian password | |
dc.subject.en | password meters | |
dc.subject.en | zxcvbn | |
dcterms.sourcetitle | Applied sciences: Special issue: Data-driven cybersecurity and privacy analysis | |
dc.description.issue | iss. 13 | |
dc.description.volume | vol. 13 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 001028485600001 | |
dc.identifier.doi | 10.3390/app13137811 | |
dc.identifier.elaba | 171039221 | |