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dc.contributor.authorGurčinas, Vitalijus
dc.contributor.authorDautartas, Juozas
dc.contributor.authorJanulevičius, Justinas
dc.contributor.authorGoranin, Nikolaj
dc.contributor.authorČenys, Antanas
dc.date.accessioned2023-09-18T16:39:33Z
dc.date.available2023-09-18T16:39:33Z
dc.date.issued2023
dc.identifier.issn2079-9292
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115548
dc.description.abstractInvestigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks have been an issue due to the limitations of hardware and software up until recently. This paper presents comprehensive research on keystroke-injection payload generation that proposes the use of deep learning to bypass the security of keystroke-based authentication systems focusing on both fixed-text and free-text scenarios. In addition, it specifies the potential risks associated with keystroke-injection attacks. To ensure the legitimacy of the investigation, a model is proposed and implemented within this context. The results of the implemented implant model inside the keyboard indicate that deep learning can significantly improve the accuracy of keystroke dynamics recognition as well as help to generate effective payload from a locally collected dataset. The results demonstrate favorable accuracy rates, with reported performance of 93–96% for fixed-text scenarios and 75–92% for free-text. Accuracy across different text scenarios was achieved using a small dataset collected with the proposed implant model. This dataset enabled the generation of synthetic keystrokes directly within a low-computation-power device. This approach offers efficient and almost real-time keystroke replication. The results obtained show that the proposed model is sufficient not only to bypass the fixed-text keystroke dynamics system, but also to remotely control the victim’s device at the appropriate time. However, such a method poses high security risks when deploying adaptive keystroke injection with impersonated payload in real-world scenarios.eng
dc.formatPDF
dc.format.extentp. 1-29
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyINSPEC
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://www.mdpi.com/2079-9292/12/13/2894
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:170880559/datastreams/MAIN/content
dc.titleA deep-learning-based approach to keystroke-injection payload generation
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis 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.licenseCreative Commons – Attribution – 4.0 International
dcterms.references70
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionE09 - Elektronikos inžinerija / Electronic 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.enkeystroke dynamics
dc.subject.enmachine learning
dc.subject.endeep learning
dc.subject.enbehavioral biometrics
dc.subject.enkeystroke recognition
dcterms.sourcetitleElectronics: Special issue: Data driven security
dc.description.issueiss. 13
dc.description.volumevol. 12
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi001028585700001
dc.identifier.doi10.3390/electronics12132894
dc.identifier.elaba170880559


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