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AI-based user authentication reinforcement by continuous extraction of behavioral interaction features
dc.contributor.author | Garabato, D. | |
dc.contributor.author | Dafonte, Carlos | |
dc.contributor.author | Santovena, Raul | |
dc.contributor.author | Silvelo, Arturo | |
dc.contributor.author | Nóvoa, Francisco | |
dc.contributor.author | Manteiga, Minia | |
dc.date.accessioned | 2023-04-03T13:51:11Z | |
dc.date.available | 2023-04-03T13:51:11Z | |
dc.date.issued | 2022-07 | |
dc.identifier.citation | D. Garabato, C. Dafonte, R. Santoveña, A. Silvelo, F. J. Nóvoa, y M. Manteiga, «AI-based user authentication reinforcement by continuous extraction of behavioral interaction features», Neural Comput & Applic, vol. 34, n.º 14, pp. 11691-11705, jul. 2022, doi: 10.1007/s00521-022-07061-3. | es_ES |
dc.identifier.issn | 1433-3058 | |
dc.identifier.uri | http://hdl.handle.net/2183/32823 | |
dc.description | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. | es_ES |
dc.description.abstract | [Abstract]: In this work, we conduct an experiment to analyze the feasibility of a continuous authentication method based on the monitorization of the users' activity to verify their identities through specific user profiles modeled via Artificial Intelligence techniques. In order to conduct the experiment, a custom application was developed to gather user records in a guided scenario where some predefined actions must be completed. This dataset has been anonymized and will be available to the community. Additionally, a public dataset was also used for benchmarking purposes so that our techniques could be validated in a non-guided scenario. Such data were processed to extract a number of key features that could be used to train three different Artificial Intelligence techniques: Support Vector Machines, Multi-Layer Perceptrons, and a Deep Learning approach. These techniques demonstrated to perform well in both scenarios, being able to authenticate users in an effective manner. Finally, a rejection test was conducted, and a continuous authentication system was proposed and tested using weighted sliding windows, so that an impostor could be detected in a real environment when a legitimate user session is hijacked. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2021/36 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A-2019/155 | es_ES |
dc.description.sponsorship | This work made use of the infrastructures acquired with Grants provided by the State Research Agency (AEI) of the Spanish Government and the European Regional Development Fund (FEDER), through RTI2018-095076-B-C22, and PID2019-525 111388GB-I00. We acknowledge support from CIGUS-CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) through Grant ED431G 2019/01; research consolidation Grant ED431B 2021/36; Art.83 collaboration F19/03 with the enterprise Odeene S.L.; and scholarship from Xunta de Galicia and the European Union (European Social Fund - ESF) ED481A-2019/155. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095076-B-C22/ES/MINERIA DE DATOS DE GAIA PARA ESTUDIAR LA VIA LACTEA II | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWARE | es_ES |
dc.relation.uri | https://doi.org/10.1007/s00521-022-07061-3 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Behavioral features | es_ES |
dc.subject | Second level authentication | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Deep learning | es_ES |
dc.title | AI-based user authentication reinforcement by continuous extraction of behavioral interaction features | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Neural computing & applications | es_ES |
UDC.volume | 34 | es_ES |
UDC.issue | 14 | es_ES |
UDC.startPage | 11691 | es_ES |
UDC.endPage | 11705 | es_ES |
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