A Secure Authentication Model for Enhancing Single Sign-On Security against Credential Compromise

Abstract

Biometric data, such as fingerprints has the potential to mitigate risks, enhance user experience, and inform cybersecurity practices. Finding SSO vulnerabilities is the first step in the research objectives' well-defined process that also includes designing and implementing an authentication model and finally evaluating how well it prevents unauthorized access. By pursuing these objectives, the research intends to deliver practical guidance not just to enterprises and end-users, but also to security practitioners and policy makers. The study will employ systematic literature review to identify weaknesses in existing SSO systems. The study will utilize design thinking to design the biometric authentication model. Implementation of the model will involve the use of WebAuthn that enables secure biometric authentication through two core processes namely, registration and authentication. During enrollment, a public/private key pair is generated, and the public key is stored on the server after verification. In the authentication phase, the user's authenticator signs a challenge using the private key, and the server verifies it using the public key ensuring secure, password less access. The model will be tested using both real and sample biometric data. Mobile devices with inbuilt fingerprint and facial recognition will simulate user login, while fingerprint data sets from Fingerprint Verification Competition 2004 database will be used to validate the model. The study will evaluate the effectiveness of the authentication model using Goal-based evaluation methodology. The data collection procedure will ensure ethical considerations are adhered to by the Researcher and information gathered is voluntary and anonymous. Tools for data collection will involve other researcher’s work publications and reports to gather information and extract relevant data for analysis. Analysis of data will be performed using thematic analysis.

Keywords: Biometric authentication, security, single sign-on