Abstract
User authentication is the critical first step to detect identity-based attacks and prevent subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, we explore to use channel state information (CSI), which is available from off-the-shelf WiFi devices, to conduct fine-grained user authentication. We propose an user-authentication framework that has the capability to build the user profile resilient to the presence of the spoofer. Our machine learning based user-authentication techniques can distinguish two users even when they possess similar signal fingerprints and detect the existence of the spoofer. Our experiments in both office building and apartment environments show that our framework can filter out the signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).