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Discriminative Power of Typing Features on Desktops, Tablets, and Phones for User Identification
ACM Transactions on Privacy and Security ( IF 2.3 ) Pub Date : 2020-04-04 , DOI: 10.1145/3377404
Amith K. Belman 1 , Vir V. Phoha 1
Affiliation  

Research in Keystroke-Dynamics (KD) has customarily focused on temporal features without considering context to generate user templates that are used in authentication. Additionally, work on KD in hand-held devices such as smart-phones and tablets have shown that these features alone do not perform satisfactorily for authentication. In this work, we analyze the discriminatory power of the most-used conventional features found in the literature, propose a set of context-sensitive or word-specific features, and analyze the discriminatory power of proposed features using their classification results. To perform these tasks, we use the keystroke data consisting of over 650K keystrokes, collected from 20 unique users during different activities on desktops, tablets, and phones, over a span of two months. On an average, each user made 12.5K, 9K, and 10K keystrokes on desktop, tablet, and phone, respectively. We find that the conventional features are not highly discriminatory on desktops and are only marginally better on hand-held devices for user identification. By using information of the context, a subset (derived after analysis) of our proposed word-specific features offers superior discrimination among users on all devices. We find that a majority of the classifiers, built using these features, perform user identification well with accuracies in the range of 90% to 97%, average precision and recall values of 0.914 and 0.901, respectively, on balanced test samples in 10-fold cross validation. We also find that proposed features work best on hand-held devices. This work calls for a shift from using conventional KD features to a set of context-sensitive or word-specific KD features that take advantage of known information such as context.

中文翻译:

台式机、平板电脑和手机上用于用户识别的打字功能的辨别力

Keystroke-Dynamics (KD) 的研究通常专注于时间特征,而不考虑上下文来生成用于身份验证的用户模板。此外,在智能手机和平板电脑等手持设备中对 KD 的研究表明,仅凭这些功能无法令人满意地进行身份验证。在这项工作中,我们分析了文献中最常用的常规特征的判别力,提出了一组上下文敏感或特定于单词的特征,并使用它们的分类结果分析了所提出特征的判别力。为了执行这些任务,我们使用了包含超过 65 万次击键的击键数据,这些击键数据是在两个月内从 20 位唯一用户在台式机、平板电脑和手机上的不同活动中收集的。平均每个用户赚了 12.5K、9K、和 10K 键击分别在台式机、平板电脑和手机上。我们发现传统功能在台式机上的识别性并不高,在用于用户识别的手持设备上仅略胜一筹。通过使用上下文信息,我们提出的特定单词特征的子集(分析后得出)在所有设备上的用户之间提供了卓越的区分。我们发现,使用这些特征构建的大多数分类器在 10 倍平衡测试样本上以 90% 到 97% 范围内的准确度、0.914 和 0.901 的平均精度和召回值分别执行用户识别交叉验证。我们还发现提议的功能在手持设备上效果最好。
更新日期:2020-04-04
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