请稍等:使用智能手机内置传感器的低调用户认证

Attaullah Buriro, B. Crispo, Yury Zhauniarovich
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引用次数: 43

摘要

智能手机提供随时随地的通信,并且越来越多地用于各种目的,例如发送电子邮件,进行在线交易,通过社交网络与朋友和熟人联系。因此,大量敏感的个人信息经常被生成并存储在智能手机上。因此,如果这些信息落入坏人之手,智能手机用户可能会面临经济和情感上的后果。为了解决这个问题,所有智能手机都提供了某种形式的用户身份验证,即验证用户身份的过程。现有的身份验证机制(例如使用4位密码或图形模式)存在多种限制——它们既不高度安全,也不容易输入。因此,最近的研究发现,大多数智能手机用户根本不使用任何身份验证机制。在本文中,我们提出了一种完全不引人注目的用户身份验证方案,该方案基于用户解锁智能手机后用户手部的微运动。该方案从多个三维智能手机传感器在后台收集特定时间段的数据,并根据收集到的手部运动模式对用户进行分析。随后,系统将查询模式与预先存储的模式进行匹配,以验证智能手机所有者的身份。我们的系统在31名合格志愿者(总共53人)的数据集上,使用随机森林(RF)分类器,实现了96%的真实接受率(TAR)和4%的平均错误率(EER)。我们的方案可以用作主要的身份验证机制,也可以与任何现有的身份验证方案(例如密码)一起用作辅助身份验证方案,以提高其安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Please hold on: Unobtrusive user authentication using smartphone's built-in sensors
Smartphones provide anytime-anywhere communications and are being increasingly used for a variety of purposes, e.g, sending email, performing online transactions, connecting with friends and acquaintances over social networks. As a result, a considerable amount of sensitive personal information is often generated and stored on smartphones. Thus, smartphone users may face financial as well as sentimental consequences if such information fall in the wrong hands. To address this problem all smartphones provide some form of user authentication, that is the process of verifying the user's identity. Existing authentication mechanisms, such as using 4-digit passcodes or graphical patterns, suffer from multiple limitations - they are neither highly secure nor easy to input. As a results, recent studies found that most smartphone's users do not use any authentication mechanism at all. In this paper, we present a fully unobtrusive user authentication scheme based on micro-movements of the user's hand(s) after the user unlocks her smartphone. The proposed scheme collects data from multiple 3-dimensional smartphone sensors in the background for a specific period of time and profiles a user based on the collected hand(s) movement patterns. Subsequently, the system matches the query pattern with the pre-stored patterns to authenticate the smartphone owner. Our system achieved a True Acceptance Rate (TAR) of 96% at an Equal Error Rate (EER) of 4%, on a dataset of 31 qualified volunteers (53, in total), using Random Forest (RF) classifier. Our scheme can be used as a primary authentication mechanism or can be used as a secondary authentication scheme in conjunction with any of the existing authentication schemes, e.g., passcodes, to improve their security.
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