首页|Research Data from North Carolina Agricultural and Technical State University Update Understanding of Machine Learning (Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two- Factor Authentication)
Research Data from North Carolina Agricultural and Technical State University Update Understanding of Machine Learning (Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two- Factor Authentication)
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NSTL
IEEE
Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Greensboro, North Carolina, by NewsRx editors, research stated, "The traditional twofactor 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." Our news correspondents obtained a quote from the research from North Carolina Agricultural and Technical State University: "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."
North Carolina Agricultural and Technical State UniversityGreensboroNorth CarolinaUnited StatesNorth and Central AmericaCybersecurityCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
