Conditional Generative Adversarial Network for keystroke presentation attack

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

“…Direct comparison of our results with studies conducted by other researchers poses challenges due to divergent research focuses. Some researchers have prioritized hardware implementation for keystroke-injection attacks [24], while others have focused on software implementation [47]. Notably, in terms of user-classification accuracy with free-text, their results ranged from 94% to 97%, closely aligning with the outcomes of our own research.…”

“…These systems are used for continuous authentication and are often used to defend against keystroke-injection attacks. The evaluation of accuracy and performance in free-text keystroke dynamics is an ongoing area of investigation; researchers are actively developing methods to minimize error rates in this domain [46], while other researchers have explored the application of cGAN networks to generate fake keystroke dynamics patterns with the intention of deceiving keystroke-authentication systems [47]. Given the growing need for remote learning, continuous authentication with keystroke dynamics was implemented and performed very effectively [48], although keystroke dynamics has significant inaccuracies when applied to RDP (remote desktop protocol) and VNC (virtual network computing) systems, resulting in poor or non-functioning operation.…”

“…Later, these results are compared. In contrast, generative adversarial neural networks are also gaining momentum for attacks against user identification systems [47,50]. In this architecture, two neural networks work against each other.…”