Keystroke biometrics is a promising approach for user identification and verification, leveraging the unique patterns in individuals' typing behavior. In this paper, we propose a Transformer-based network that employs self-attention to extract informative features from keystroke sequences, surpassing the performance of traditional Recurrent Neural Networks. We explore two distinct architectures, namely bi-encoder and cross-encoder, and compare their effectiveness in keystroke authentication. Furthermore, we investigate different loss functions, including triplet, batch-all triplet, and WDCL loss, along with various distance metrics such as Euclidean, Manhattan, and cosine distances. These experiments allow us to optimize the training process and enhance the performance of our model. To evaluate our proposed model, we employ the Aalto desktop keystroke dataset. The results demonstrate that the bi-encoder architecture with batch-all triplet loss and cosine distance achieves the best performance, yielding an exceptional Equal Error Rate of 0.0186%. Furthermore, alternative algorithms for calculating similarity scores are explored to enhance accuracy. Notably, the utilization of a one-class Support Vector Machine reduces the Equal Error Rate to an impressive 0.0163%. The outcomes of this study indicate that our model surpasses the previous state-of-the-art in free-text keystroke authentication. These findings contribute to advancing the field of keystroke authentication and offer practical implications for secure user verification systems.

翻译：击键生物特征识别是一种有前景的用户身份识别与验证方法，它利用个体打字行为中的独特模式。本文提出一种基于Transformer的网络，该网络采用自注意力机制从击键序列中提取信息特征，其性能超越了传统循环神经网络。我们探索了两种不同的架构，即双向编码器和交叉编码器，并比较了它们在击键认证中的有效性。此外，我们研究了多种损失函数，包括三元组损失、批量全三元组损失和WDCL损失，以及不同的距离度量（如欧氏距离、曼哈顿距离和余弦距离）。这些实验使我们能够优化训练过程并提升模型性能。为评估所提模型，我们采用了Aalto桌面击键数据集。结果表明，采用批量全三元组损失和余弦距离的双向编码器架构表现最佳，等错误率低至0.0186%。此外，我们还探索了计算相似度得分的替代算法以提高准确性。值得注意的是，使用一类支持向量机将等错误率进一步降低至0.0163%。本研究结果表明，我们的模型在自由文本击键认证中超越了先前的最优方法。这些发现推动了击键认证领域的发展，并为安全的用户验证系统提供了实际应用价值。