I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics
I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics
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DOI：
10.1007/978-3-319-08509-8_6
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发表时间：
2014-07
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影响因子：
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通讯作者：
Cristiano Giuffrida;Kamil Majdanik;M. Conti;H. Bos
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文献类型：
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作者：
Cristiano Giuffrida;Kamil Majdanik;M. Conti;H. Bos
移动设备已成为我们日常生活的重要组成部分，获取越来越多的机密用户信息。然而，它们的便携性和易受安全攻击的风险要求比简单的基于密码的识别更强大的身份验证机制。生物识别技术在这方面已显示出潜力。不幸的是，之前的方法要么太容易被伪造，要么精度太低而无法促进广泛采用。在本文中，我们提出了传感器增强的击键动力学，这是一种新的生物识别机制，用于验证用户在移动设备上打字的身份。关键思想是通过独特的传感器功能来表征用户的打字行为，并依靠标准的机器学习技术来执行用户身份验证。为了证明我们方法的有效性，我们实现了一个名为 Unagi 的 Android 原型系统。我们的实现支持多种特征提取和检测算法，用于评估和比较目的。实验结果表明，传感器增强的击键动态可以将最新基于手势的认证机制（即 EER>0.5%）的准确性提高一个数量级，将传统击键动态（即 EER>7%）的准确性提高两个数量级。
Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption.In this paper, we proposesensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termedUnagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e.,EER>0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e.,EER>7%) by two orders of magnitude.