  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.
