This work proposes a data driven learning model for the synthesis of
keystroke biometric data. The proposed method is compared with two statistical
approaches based on Universal and User-dependent models. These approaches are
validated on the bot detection task, using the keystroke synthetic data to
improve the training process of keystroke-based bot detection systems. Our
experimental framework considers a dataset with 136 million keystroke events
from 168 thousand subjects. We have analyzed the performance of the three
synthesis approaches through qualitative and quantitative experiments.
Different bot detectors are considered based on several supervised classifiers
(Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long
Short-Term Memory network) and a learning framework including human and
synthetic samples. The experiments demonstrate the realism of the synthetic
samples. The classification results suggest that in scenarios with large
labeled data, these synthetic samples can be detected with high accuracy.
However, in few-shot learning scenarios it represents an important challenge.
Furthermore, these results show the great potential of the presented models.

本研究提出一种数据驱动的学习模型，用于合成按键生物特征数据，并与基于通用和用户相关模型的两种统计方法进行比较。通过合成生物特征数据，本研究改进了基于按键的机器人检测系统的训练过程，并通过定量和定性实验验证了三种方法的表现。实验结果表明，在有大量标记数据的情况下，这些合成样本可以被高精度地检测出来，但在少样本学习的情况下则是一个重要的挑战。此外，这些结果显示了所提出模型的巨大潜力。