Enhancing User-Centric Security using Machine Learning Techniques for Continuous Authentication using Keystroke Dynamics

In today’s world, user authentication has gained enormous popularity due to dependency on technological devices. Once the user gets access to a system and is authenticated, there are no such mechanisms to constantly verify whether the user is legitimate or illegitimate. Behavioural biometrics can be utilized for continuous user authentication, enhancing overall security. This approach includes various factors to authenticate users such as Keyboard typing patterns, movement of the mouse, adaptive authentication techniques, etc. This paper explores the integration of machine learning techniques for enhancing user-centric security through continuous authentication using keystroke dynamics. Keystroke dynamics, as a behavioural biometric, offers a non-intrusive method to verify user identity based on typing patterns. By continuously monitoring these patterns, our approach aims to strengthen authentication processes without disrupting user experience. This study investigates the application of machine learning algorithms to analyze keystroke dynamics data in real-time, enabling adaptive access control and bolstering overall cybersecurity posture.