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Improving Resilience of Behaviometric Based Continuous Authentication with Multiple Accelerometers

Abstract : Behaviometrics in multi-factor authentication schemes continuously assess behavior patterns of a subject to recognize and verify his identity. In this work we challenge the practical feasibility and the resilience of accelerometer-based gait analysis as a behaviometric under sensor displacement conditions. To improve misauthentication resistance, we present and evaluate a solution using multiple accelerometers on 7 positions on the body during different activities and compare the effectiveness with Gradient-Boosted Trees classification. From a security point of view, we investigate the feasibility of zero and non-zero effort attacks on gait analysis as a behaviometric. Our experimental results with data from 12 individuals show an improvement in terms of EER with about 2% (from 5% down to 3%), with an increased resilience against observation attacks. When trained to defend against such attacks, we observe no decrease in classification performance.
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Submitted on : Monday, January 15, 2018 - 2:06:51 PM
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Tim Hamme, Davy Preuveneers, Wouter Joosen. Improving Resilience of Behaviometric Based Continuous Authentication with Multiple Accelerometers. 31th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2017, Philadelphia, PA, United States. pp.473-485, ⟨10.1007/978-3-319-61176-1_26⟩. ⟨hal-01684348⟩



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