Continuous Authentication Leveraging Matrix Profile - Données et algorithmes pour une ville intelligente et durable
Communication Dans Un Congrès Année : 2024

Continuous Authentication Leveraging Matrix Profile

Résumé

Continuous Authentication (CA) mechanisms involve managing sensitive data from users which may change over time. Both requirements (privacy and adapting to new users) lead to a tension in the amount and granularity of the data at stake. However, no previous work has addressed them together. This paper proposes a CA approach that leverages incremental Matrix Profile (MP) and Deep Learning using accelerometer data. Results show that MP is effective for CA purposes, leading to 99% of accuracy when a single user is authorized. Besides, the model can on-the-fly increase the set of authorized users up to 10 while offering similar accuracy rates. The amount of input data is also characterized -- the last 15 sec. of data in the user device require 0.4 MB of storage and lead to a CA accuracy of 97% even with 10 authorized users.
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Dates et versions

hal-04663471 , version 1 (28-07-2024)

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  • HAL Id : hal-04663471 , version 1

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Luis Ibanez-Lissen, Jose Maria de Fuentes, Lorena Gonzales-Manzano, Nicolas Anciaux. Continuous Authentication Leveraging Matrix Profile. ARES 2024 - The 19th International Conference on Availability, Reliability and Security, Jul 2024, Vienne, Austria. ⟨hal-04663471⟩
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