Keystroke Dynamic

Keystroke dynamics analyzes the timing and rhythm of keyboard typing to identify individuals or detect anomalies in user behavior. Current research focuses on improving the accuracy and robustness of keystroke-based authentication and identification systems using various machine learning models, including convolutional neural networks, recurrent neural networks, and transformer architectures, often applied to large datasets of keystroke patterns. This field is significant for enhancing cybersecurity, improving online exam proctoring, and even assisting in mental health applications by passively sensing user emotional states through typing patterns. The development of standardized benchmarks and datasets is also a key area of ongoing work.