Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication

Abstract

The traditional two-factor authentication (2FA) methods primarily rely on the user manually entering a code or token during the authentication process. This can be burdensome and time-consuming, particularly for users who must be authenticated frequently. To tackle this challenge, we present a novel 2FA approach replacing the user's input with decisions made by Machine Learning (ML) that continuously verifies the user's identity with zero effort. Our system exploits unique environmental features associated with the user, such as beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi Access Points (APs). These features are gathered and analyzed in real-time by our ML algorithm to ascertain the user's identity. For enhanced security, our system mandates that the user's two devices (i.e., a login device and a mobile device) be situated within a predetermined proximity before granting access. This precaution ensures that unauthorized users cannot access sensitive information or systems, even with the correct login credentials. Through experimentation, we have demonstrated our system's effectiveness in determining the location of the user's devices based on beacon frame characteristics and RSSI values, achieving an accuracy of 92.4%. Additionally, we conducted comprehensive security analysis experiments to evaluate the proposed 2FA system's resilience against various cyberattacks. Our findings indicate that the system exhibits robustness and reliability in the face of these threats. The scalability, flexibility, and adaptability of our system render it a promising option for organizations and users seeking a secure and convenient authentication system.INDEX TERMS two-factor authentication, machine learning, zero effort, continuous authentication, beacon frames, wireless access points, authentication module, Wi-Fi radio waves, RSSI values, 2FA, ML.