Wi-Sign: Device-free Second Factor User Authentication

Most two-factor authentication (2FA) implementations rely on the user possessing and interacting with a secondary device (e.g. mobile phone) which has contributed to the lack of widespread uptake. We present a 2FA system, called Wi-Sign that does not rely on a secondary device for establishing the second factor. The user is required to sign at a designated place on the primary device with his finger following a successful first step of authentication (i.e. username + password). Wi-Sign captures the unique perturbations in the WiFi signals incurred due to the hand motion while signing and uses these to establish the second factor. Wi-Sign detects these perturbations by measuring the fine-grained Channel State Information (CSI) of the ambient WiFi signals at the device from which log-in attempt is being made. The logic is that, the user's hand geometry and the way he moves his hand while signing cause unique perturbations in CSI time-series. After filtering noise from the CSI data, principal component analysis is employed for compressing the CSI data. For segmentation of sign related perturbations, Wi-Sign utilizes the thresholding approach based on the variance of the first-order difference of the selected principal component. Finally, the authentication decision is made by feeding scrupulously selected features to a One-Class SVM classifier. We implement Wi-Sign using commodity off-the-shelf 802.11n devices and evaluate its performance by recruiting 14 volunteers. Our evaluation shows that Wi-Sign can on average achieve 79% TPR. Moreover, Wi-Sign can detect attacks with an average TNR of 86%.