Authenticating Users Through Fine-Grained Channel Information - 2018


User authentication is that the critical initial step in detecting identity-based attacks and preventing subsequent malicious attacks. But, the increasingly dynamic mobile environments make it harderto forever apply cryptographic-based strategies 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 seems promising. During this work, the utilization of channel state info (CSI), that is accessible from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques will distinguish between two users even after they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both workplace building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).

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