Analysis of Algorithms for User Authentication using Keystroke Dynamics

In the present scenario, security is the biggest concern in any domain of applications. The latest and widely used system for user authentication is a biometric system. This includes fingerprint recognition, retina recognition, and voice recognition. But these systems can be bypassed by masqueraders. To avoid this, a combination of these systems is used which becomes very costly. To overcome these two drawbacks keystroke dynamics were introduced in this field. Keystroke dynamics is a biometric authentication-based system on behavior, which is an automated method in which the identity of an individual is identified and confirmed based on the way and the rhythm of passwords typed on a keyboard by the individual. The work in this paper focuses on identifying the best algorithm for implementing an authentication system with the help of machine learning for user identification based on keystroke dynamics. Our proposed model which uses XGBoost gives a comparatively higher accuracy of 93.59% than the other algorithms for the dataset used.