AI-based user authentication reinforcement by continuous extraction of behavioral interaction features
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AI-based user authentication reinforcement by continuous extraction of behavioral interaction featuresAutor(es)
Data
2022-07Cita bibliográfica
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.
Resumo
[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.
Palabras chave
Behavioral features
Second level authentication
Neural networks
Deep learning
Second level authentication
Neural networks
Deep learning
Descrición
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1433-3058