Touch to Authenticate — Continuous Biometric Authentication on Mobile Devices

Marlies Temper, S. Tjoa, Manfred Kaiser
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引用次数: 16

Abstract

Like no other device, smart phones influence our way of communication. The benefits of combining a variety of functionality (e.g. taking photos, navigating, surfing the web and exchanging information) into a single device led to a rich repository of private and commercial information. Although the information on smart phones in many cases is highly sensitive for either privacy or business reasons most users use simple and ineffective passcodes or patterns as authentication method. The security situation becomes even more complicated if companies encourage their employees by Bring-Your-Own-Device policies to shift workload to smart phones without a holistic security concept. As strong authentication methods will only be successful if they comply with the usability requirements of today’s users, biometric approaches pose an opportunity to close the gap between security and usability.In this paper we contribute to this research field by introduc- ing an approach for continuous biometric authentication using touchscreen gestures and related posture information as unique features. In first experiments, this new authentication layer for Android-based phones, which is using a fuzzy classifier in combination with a scoring model, demonstrated its feasibility by achieving an Equal Error Rate (EER) of 11,5%.
触摸认证-移动设备上的连续生物识别认证
与其他设备不同,智能手机影响着我们的交流方式。将多种功能(如拍照、导航、上网和信息交换)结合到一个设备中的好处,导致了丰富的私人和商业信息库。尽管智能手机上的信息在许多情况下出于隐私或商业原因是高度敏感的,但大多数用户使用简单而无效的密码或模式作为身份验证方法。如果企业在没有整体安全概念的情况下,通过“自带设备”政策鼓励员工将工作转移到智能手机上,那么安全形势将变得更加复杂。由于强身份验证方法只有在符合当今用户的可用性要求时才会成功,因此生物识别方法提供了缩小安全性和可用性之间差距的机会。在本文中,我们通过引入一种使用触摸屏手势和相关姿势信息作为独特特征的连续生物识别认证方法,为这一研究领域做出了贡献。在第一个实验中,这种基于android手机的新身份验证层使用模糊分类器和评分模型相结合,通过实现11.5%的等错误率(EER)来证明其可行性。
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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