Password-based authentication system has been the most popular method for authenticating users in computing devices and system access, where keyboards are used to input PINs, tokens, or passwords. However, it becomes more fragile for weak passwords and prone to numerous attacks such as dictionary attacks, guessing attacks, and shoulder surfing attacks.

In this work, we developed a keystroke dynamics based hybrid self-powered sensors for biometric authentication and identification integrated with AI. Keystroke dynamics offer behavioral and contextual information that can distinguish and authorize the individuals based on their typing rhythms. Our system achieves an accuracy of 99% and offers a promising hybrid security layer against password vulnerability.