Open AccessModel and Algorithms for User Identification by Network Traffic
Author(s) -
V. E. Gai,
Irina Ephode,
Roman Barinov,
Igor V. Polyakov,
Vladimir Golubenko,
Olga Andreeva
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.20948/graphicon-2021-3027-1017-1027
Subject(s) - computer science , support vector machine , identification (biology) , network packet , data mining , hyperplane , machine learning , traffic classification , decision tree , boosting (machine learning) , algorithm , artificial intelligence , computer network , botany , geometry , mathematics , biology
This paper proposes a method of user identification by network traffic. We describe the information model created, as well as the implementation of each of the proposed problem solving stages. During the network traffic collection stage, a method of capturing network packets on the user's device using specialized software is used. The information obtained is further filtered by removing redundant data. During the object feature descriptor construction stage, we extract and describe the characteristics of network sessions from which the behavioral habits of users are derived. Classification of users according to the extracted characteristics of the network sessions is performed using machine learning techniques. When analyzing the test results, the most appropriate machine learning algorithms for solving the problem of user identification by network traffic were proposed, such as: logistic regression, decision trees, SVM with a linear hyperplane and the boosting method. The accuracy of the above methods was more than 95%. The results proved that it is possible to identify a particular user with a sufficiently high accuracy based on the characteristics of the data transmitted through the network, without examining the contents of the transmitted packets. Comparison of the developed model has shown that the proposed model of user identification by network traffic works as effectively as the existing analogues.