How Artificial Intelligence can be used for Behavioral Identification?
| Autor: | Yris Brice Wandji Piugie, Christophe Rosenberger, Christophe Charrier, Joel Di Manno |
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| Přispěvatelé: | Equipe SAFE - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), FIME EMEA, WANDJI PIUGIE, Yris Brice |
| Předmět: |
Biometrics
Computer science [SPI] Engineering Sciences [physics] [INFO] Computer Science [cs] privacy [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [SPI]Engineering Sciences [physics] User experience design Human–computer interaction Information system [INFO]Computer Science [cs] Behavioral biometrics Modality (human–computer interaction) Modalities business.industry Deep learning deep learning [STAT] Statistics [stat] [STAT]Statistics [stat] Identification (information) Keystroke dynamics machine learning identification keystroke dynamics Artificial intelligence business |
| Zdroj: | HAL 2021 International Conference on Cyberworlds (CW) 2021 International Conference on Cyberworlds (CW), Sep 2021, Caen, France CW |
| Popis: | International audience; Nowadays, users interact with computer systems. Behavioral biometrics consists of analyzing user's interactions for identification and verification applications. This approach could be very useful for enhancing security and improving user experience and many privacy concerns are also related. In this paper, we address the problem of user identification considering their behaviors. How efficient are classical machine learning methods on such data? What about deep learning approaches? We illustrate this work on two behavioral modalities namely human activity using smartphones and keystroke dynamics on a laptop. Since the accuracy rates of most behavioral biometrics modalities are lower than morphological ones, we consider two approaches for these modalities that can be represented as time series: classical machine learning and deep learning techniques. We intend to show that many algorithms can obtain very good performance for different modalities without any specific tuning to the considered modality. This comparative analysis allows us to show that behavioral biometrics can be used for security applications (i.e. who is accessing the company information system) but could be a privacy concern as a user could be identified while navigating on the Internet. |
| Databáze: | OpenAIRE |
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