Authentication using Static and Continuous Keystroke Dynamics in Classical and Quantum Environments

Static keystroke dynamics helps in secure access to the system as the user is authenticated based on username/password typing rhythm at the time of login. Continuous keystroke dynamics is an amazing concept where the user is authenticated after login. This paper solves the problem of static keystroke dynamics by image classification in classical and quantum environments. The static keystroke dynamics dataset is converted into images using the Markov transition field, which is classified by classical and quantum algorithms. It is found that classification accuracy is 7% more in quantum than classical algorithm. For continuous keystroke dynamics, classical algorithms performance is the equal or better than the quantum algorithms.