Posture and Body Movement Effects on Behavioral Biometrics for Continuous Smartphone Authentication

Nicholas Cariello;Robert Eslinger;Rosemary Gallagher;Isaac Kurtzer;Paolo Gasti;Kiran S. Balagani
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Abstract

Continuous authentication aims to authenticate users at regular intervals post-login, typically using biometric features that capture the user’s behavior. One of the drawbacks of continuous authentication is that it usually introduces a high authentication latency, i.e., behavioral features need to be captured for 45–120 seconds in order to achieve acceptable authentication error rates. In this paper, we take a step towards addressing this problem by harnessing 3D motion capture data and creating an extensive set of body motion and posture features with the goal of achieving low authentication error rates with short (1–5 second) authentication latencies. To evaluate our features, we collected a dataset from 39 users engaged in a set of smartphone tasks performed in a 3D motion capture studio. To collect our data, we placed 41 IR-reflective markers on the subjects’ body and 3 on the smartphone. The markers were tracked by 3D motion capture cameras. During data collection, subjects were either walking along a pre-determined path or sitting. We show that our features can lead to a low equal error rate (EER) of 6.4% with 1-second latency, and 5.4% with 5-second latency. In contrast, under the same experimental settings, swipe and phone-movement features alone led to an EER of 15.7% for a 60-second authentication latency. While our features demonstrate the potential to achieve low authentication error with very low authentication latencies, we envision that in practice these features will be collected using standard smartphone sensors and consumer-grade wearable devices. We believe that our results hold transformative potential, because they shift continuous authentication from a reactive (i.e., detection is successfully performed well into the attack) to a proactive security measure (i.e., detection happens as the attack starts). As part of our contributions, we have made the dataset used in this paper publicly available.
姿势和身体运动对智能手机连续认证行为生物识别的影响
持续身份验证的目的是在登录后定期对用户进行身份验证,通常使用捕捉用户行为的生物特征。持续身份验证的缺点之一是,它通常会引入较高的身份验证延迟,也就是说,为了达到可接受的身份验证错误率,需要捕获45-120秒的行为特征。在本文中,我们通过利用3D动作捕捉数据并创建一套广泛的身体运动和姿势特征来解决这个问题,目标是在短(1-5秒)的身份验证延迟中实现低身份验证错误率。为了评估我们的特征,我们收集了39个用户的数据集,这些用户参与了一组在3D动作捕捉工作室执行的智能手机任务。为了收集数据,我们在受试者身上放置了41个红外反射标记,在智能手机上放置了3个。这些标记由3D动作捕捉摄像机跟踪。在数据收集过程中,受试者要么沿着预定的路径行走,要么坐着。我们表明,我们的特征可以导致较低的等错误率(EER),在1秒延迟时为6.4%,在5秒延迟时为5.4%。相比之下,在相同的实验设置下,在60秒的认证延迟中,仅滑动和移动手机功能就导致了15.7%的EER。虽然我们的功能展示了实现低身份验证错误和极低身份验证延迟的潜力,但我们设想在实践中,这些功能将使用标准智能手机传感器和消费级可穿戴设备收集。我们相信我们的结果具有变革潜力,因为它们将持续身份验证从被动的(即,在攻击中成功执行检测)转变为主动的安全措施(即,在攻击开始时进行检测)。作为我们贡献的一部分,我们已经公开了本文中使用的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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