A COMPUTER MODEL OF INFORMATION VIRUS PROPAGATION IN A SOCIAL NETWORK WITH DIFFERENT USER BEHAVIOR

Abstract

Nowadays, modeling the processes of the spread of information viruses is an important task of cybersecurity, because it is necessary to clearly distinguish where the truth is from where it is fake, to be able to identify the sourc e of the spread of fake news and to counter disinformation in order to convey the truth to people. The purpose of this work was to create and research a computer model of information virus propagation in a social network with different user behavior. The SIRS epidemiological model and the Barabási-Albert and Watts-Strogatz social network structure generation models were used to achieve this goal. The SIRS model is ideal for simulating the spread of a computer virus, because in this model a person can cycle through three states: susceptible, infected, and recovered with immunity, analogous to how a user in a social network can be "infected" to and be "cured" of a fake. Barabási-Albert and Watts-Strogatz algorithms, which are available in the Networkx library of the Python programming language, were used to model the structure of the social network. Several different ways of user behavior have been proposed to protect against information viruses, including removing connections between users, removing users from the network, and blocking users for suspicious activity. An empirical research and comparison of the proposed methods of combating the information virus was carried out according to various criteria. The initial parameters of the network were proposed, namely, the number of users, the number of connections between them, and the coefficients of the SIRS model. Using the Python programming language and the Pygame and Networkx libraries, the proposed model of the spread of an information virus in a social network was implemented and such methods of combating fakes as: deleting connections between users, creating a new connection, deleting users, and blocking users – were simulated. We get the best result in the fight against the information virus when we combine the methods of deleting connections and users, as well as blocking users. With the proposed user behavior, the information virus managed to successfully counteract and detect the spreader of the fake and remove it, while the number of connections between users of the social network decreased not very significantly.