Continuous Authentication Leveraging Matrix Profile

Continuous Authentication Leveraging Matrix Profile

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연구 분야: Analysis

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학회: ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

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초록

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 s. 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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