Web user identification based on browser fingerprints using machine learning methods

Abstract

Abstract The article developed a method for identifying users on the network based on browser fingerprints using machine learning methods. The resulting method is a modification of the user identification method based on a digital footprint, which can be more efficient due to two components. First, the selection of attributes for a digital footprint is made from a limited set of attributes to form a user browser fingerprint. Secondly, the identification accuracy can be increased through the combined use of classification methods and the probabilistic-statistical approach. To check the successful operation of the method, a computational experiment is carried out on real data, which consists in solving the problem of classifying a user based on his browser fingerprint using the K nearest neighbors method.