Robust Identification of Browser Fingerprint Comparison Using Edit Distance

Abstract

Web browser fingerprinting is a method currently used to identify a user's device based on the features of the device and a browser. Although this method has been used for Web tracking, its utilization for risk-based authentication or forensics is expected. However, our studies reveal that some features used in fingerprinting undergo short-term changes, which makes continuous device identification difficult. In this paper, we propose a method for identifying features before the change and after the change based on degrees of similarity. Thus, we demonstrate that continuous identification is possible even if the features change. In the datasets that we collected, when using only the information of a plugin list installed in a device, our method identifies whether the same or a different device is used with 97.94% or 97.95% accuracy, respectively.