Dataset for An Ensemble Method for Keystroke Dynamics Authentication in Free-Text Using Word Boundaries

Dataset used in the article "An Ensemble Method for Keystroke Dynamics Authentication in Free-Text Using Word Boundaries". For each user and free-text sample of the companion dataset LSIA, contains a CSV file with the list of words in the sample that survived the filters described in the article, together with the CSV files with training instances for each word. The source data comes from a dataset used in previous studies by the authors. The language of the free-text samples is Spanish.

We introduce a novel ensemble method for keystroke dynamics authentication in free-text using word boundaries and individual classifiers. In contrast with other state-of-the-art methods which reach acceptable error rates with very little training, our method demands a large training set, in the order of 50.000 characters, but reaches a lower EER, around 2.5%, when evaluated in real-world conditions. Combining both approaches in a mixed scheme allows both objectives to be achieved; the first is used in the beginning when the training data is scarce, while the second excels after enough samples are available.

本文所提及的数据集应用于《基于单词边界在自由文本中运用键击动态认证的集成方法》一文中。针对LSIA伴随数据集中每位用户的自由文本样本，包含一个CSV文件，列出了样本中经过文章所述过滤器筛选的单词列表，以及每个单词的训练实例CSV文件。原始数据来源于作者先前研究中使用的数据库。自由文本样本的语言为西班牙语。

本团队提出一种创新的集成方法，用于基于单词边界和个体分类器的自由文本键击动态认证。与其他在极少量训练数据下即可达到可接受错误率的顶尖方法相比，我们的方法需要庞大的训练集，大约50,000字符的规模，但在实际应用场景中评估时，其等错误率（EER）约为2.5%，表现更为优越。将两种方法结合于混合方案中，可实现双重目标；初始阶段当训练数据稀缺时采用第一种方法，而第二种方法则在样本数量充足后表现出色。