Synthetic Keystroke Dynamics Generation Using a Generative Adversarial Network GAN

Keystroke dynamics, a behavioral biometric modality, offers promising applications in authentication and intrusion detection systems. However, the scarcity of publicly available datasets due to privacy concerns limits research progress. This paper presents a Generative Adversarial Network (GAN) framework to generate synthetic keystroke dynamics data that closely mimics real-world patterns. Using the DSL-StrongPasswordData dataset, we pre-process and normalize timing features and train a GAN with 100-dimensional latent space, LeakyReLU activations, and binary cross-entropy loss. We evaluated the synthetic data through visual comparisons (boxplots, t-SNE projections) and statistical tests (Kolmogorov-Smirnov), demonstrating that the generated distributions align with real data (p-value > 0.05 for key features). Our results highlight the potential of the GAN for sharing data that preserve privacy and increase training sets for keystroke-based models.