Properties of Malicious Social Bots
https://doi.org/10.31854/1813-324X-2023-9-1-94-104
Abstract
The paper considers the ability to describe malicious bots using their characteristics, which can be the basis for building models for recognising bot parameters and qualitatively analysing attack characteristics in social networks. The following metrics are proposed using the characteristics of VKontakte social network bots as an example: trust, survivability, price, seller type, speed, and expert quality. To extract these metrics, an approach is proposed that is based on the methods of test purchases and the Turing test. The main advantage of this approach is that it proposes to extract features from the data obtained experimentally, thereby obtaining a more reasonable estimation than the expert approach. Also, an experiment on extracting metrics from malicious bots of the VKontakte social network using the proposed approach is described, and an analysis of the metrics' dependence is carried out. The experiment demonstrates the possibility of metrics extracting and analysis. In general, the proposed metrics and the approach to their extraction can become the basis for the transition from binary attack detection in social networks to a qualitative description of the attacker and his capabilities, as well as an analysis of the evolution of bots.
Keywords
social media security,
social bots,
social engineering,
metrics,
disinformation,
fake accounts,
risk analysis
Supporting agencies: The study was supported by the Russian Science Foundation grant No. 18-71-10094.
About the Authors
M. Kolomeets
Saint-Petersburg Federal Research Center of the Russian Academy of Sciences
Russian Federation
Russian Federation
St. Petersburg, Russian Federation
A. Chechulin
Saint-Petersburg Federal Research Center of the Russian Academy of Sciences; The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
St. Petersburg, Russian Federation
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For citations:
Kolomeets M., Chechulin A. Properties of Malicious Social Bots. Proceedings of Telecommunication Universities. 2023;9(1):94-104. (In Russ.) https://doi.org/10.31854/1813-324X-2023-9-1-94-104
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