  The proliferation of sensitive information being stored online highlights the
pressing need for secure and efficient user authentication methods. To address
this issue, this paper presents a novel zero-effort two-factor authentication
(2FA) approach that combines the unique characteristics of a users environment
and Machine Learning (ML) to confirm their identity. Our proposed approach
utilizes Wi-Fi radio wave transmission and ML algorithms to analyze beacon
frame characteristics and Received Signal Strength Indicator (RSSI) values from
Wi-Fi access points to determine the users location. The aim is to provide a
secure and efficient method of authentication without the need for additional
hardware or software. A prototype was developed using Raspberry Pi devices and
experiments were conducted to demonstrate the effectiveness and practicality of
the proposed approach. Results showed that the proposed system can
significantly enhance the security of sensitive information in various
industries such as finance, healthcare, and retail. This study sheds light on
the potential of Wi-Fi radio waves and RSSI values as a means of user
authentication and the power of ML to identify patterns in wireless signals for
security purposes. The proposed system holds great promise in revolutionizing
the field of 2FA and user authentication, offering a new era of secure and
seamless access to sensitive information.
