Evaluating multi-modal mobile behavioral biometrics using public datasets

doi:10.1016/j.cose.2022.102868

2022-10-01

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DOI：10.1016/j.cose.2022.102868

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摘要

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Behavioral biometric-based continuous user authentication is promising for securing mobile phones while complementing traditional security mechanisms. However, the existing state of art perform continuous authentication to evaluate deep learning models, but lacks examining different f eature sets over the data. Therefore, we evaluate the performance of user authentication based on acceleration, gyroscope (angu-lar velocity), and swipe data from two public mobile datasets, HMOG (Hand-Movement, Orientation, and Grasp) (Sitova et al., (2015) dataset et al. (2015)) and BB-MAS (Behavioral Biometrics Multi-device and multi-Activity data from Same users) (Belman et al., (2019) dataset et al. (2019)) extracted with differ-ent feature sets to observe the variation in authentication performance. We evaluate the performances of both individual modalities and their fusion. Since the swipe data is intermittent but the motion event data continuous, we evaluate fusion of swipes with motion events that occur within the swipes versus fusion of motion events outside of swipes. Moreover, we extract Frank et al.'s (2012) Touchalytics fea-tures Frank et al. (2012) on the swipe data but three different f eature sets (median, HMOG (Sitova et al. (2015)), and Shen's (Shen et al. (2017))) on the motion event data, among which the Shen's features were shown to perform the best. More specifically, we perform score-level fusion for a single modality utilizing binary SVMs (Support Vector Machine). Furthermore, we evaluate the fusion of multiple modalities using Nandakumar's likelihood ratio-based score fusion (Nandakumar et al. (2007)) by utilizing both one-class and binary SVMs. The best EERs (Equal Error Rates) of fusing all three modalities when using the one -class SVMs are 8.8% and 0.9% for HMOG and BB-MAS respectively. On the other hand, the best EERs in the case of binary SVMs are 1.5% and 0.2% respectively. Observing the better performances of BB-MAS com-pared to HMOG in swipe-based experiments, we examine the difference of swipe trajectory between the two datasets and find that BB-MAS has longer swipes than HMOG which would explain the performance difference in the experiments.(c) 2022 Elsevier Ltd. All rights reserved.

Keyword:

Performance evaluation

Behavioral biometric

Continuous authentication

Multi -Modality

Likelihood ratio -based score fusion

Support vector machine