了解Android图形密码模式的可用性和安全性的视觉感知

Adam J. Aviv, Dane Fichter
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引用次数: 32

摘要

本文报告了Android图形密码系统的用户研究结果,使用另一种调查方法,配对偏好,要求参与者在显示安全性或可用性偏好的成对模式之间进行选择。通过仔细选择密码对来隔离视觉特征,可以测量不同特征的可用性和安全性的视觉感知。我们使用配对偏好进行了一项大型irb批准的调查,吸引了亚马逊土耳其机械上的384名参与者。分析结果发现,复杂性的视觉特征表明了更强的安全性感知,而空间特征,如上下移动或左右移动,并不是安全性或可用性的有力指标。我们通过建立逻辑模型来扩展和应用调查数据,通过训练调查中使用的特征和相关工作中提出的其他特征来预测感知偏好。逻辑模型准确地预测了70%以上的偏好,是随机猜测率的两倍,分类中最强的特征是密码距离,即模式中所有行的总长度,这是在线调查中没有使用的特征。这个结果提供了对用户在比较选择和选择视觉密码时的内部视觉演算的洞察,这项工作的最终目标是利用视觉演算来设计系统,其中固有的可用性感知与已知的安全度量相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding visual perceptions of usability and security of Android's graphical password pattern
This paper reports the results of a user study of the Android graphical password system using an alternative survey methodology, pairwise preferences, that requests participants to select between pairs of patterns indicating either a security or usability preference. By carefully selecting password pairs to isolate a visual feature, a visual perception of usability and security of different features can be measured. We conducted a large IRB-approved survey using pairwise preferences which attracted 384 participants on Amazon Mechanical Turk. Analyzing the results, we find that visual features that can be attributed to complexity indicated a stronger perception of security, while spatial features, such as shifts up/down or left/right are not strong indicators for security or usability. We extended and applied the survey data by building logistic models to predict perception preferences by training on features used in the survey and other features proposed in related work. The logistic model accurately predicted preferences above 70%, twice the rate of random guessing, and the strongest feature in classification is password distance, the total length of all lines in the pattern, a feature not used in the online survey. This result provides insight into the internal visual calculus of users when comparing choices and selecting visual passwords, and the ultimate goal of this work is to leverage the visual calculus to design systems where inherent perceptions for usability coincides with a known metric of security.
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