Traditional authentication systems using only the username-password method are increasingly inadequate in addressing modern security threats, as they fail to adapt to dynamic risks. This project focuses on developing an adaptive authentication system using the learning state machines concept, which adjusts security protocols based on user behaviour, device type, and contextual factors, offering a more secure and adaptable approach to user authentication with a lesser usage of computational power. An adaptive authentication system was developed based on a probabilistic finite learning state machine that considers user behaviour and contextual factors to analyse the risk associated with the login attempt. Depending on the analysis, the system proceeds with the adaptive authentication to ensure a secure and user-friendly authentication process. The state machine was implemented using FlexFringe; a framework for learning automata.
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