Методы идентификации пользователей информационно-телекоммуникационной среды на основе анализа атрибутов учетных записей
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Methods of user identification in the information and telecommunication environment based on the analysis of account attributes

Romanov A.G.  

UDC 004.931
DOI: 10.26102/2310-6018/2022.38.3.002

  • Abstract
  • List of references
  • About authors

The relevance of the study is due to the problem of the growing number of unidentified persons who have committed crimes on the Internet and beyond. In this regard, the aim of the article is to demonstrate the means for personal identification by identifying users in the virtual space in order to convict them of criminal offence. The improvement of information technologies and the development of services in the information and telecommunications space provide an opportunity to analyze numerous data, including those left by users about themselves in social networks. Thus, the leading method to investigate the problem is the techniques to determine the similarity of alphanumeric objects created by users in the attributes of social network profiles. This article presents a possible algorithm of actions to deanonymize the identity of a criminal. The development and application of methods for identifying users in the virtual space will allow us to comprehensively consider the existing problem and accomplish one of the main tasks assigned to the internal affairs bodies and related to crime solving and charging perpetrators with a criminal offence. The materials of the article may be of practical value to the internal affairs bodies in the terms of enhancing the efficiency and effectiveness of law enforcement activities.

1. Perito D., Castelluccia C., Kaafar M.A., Manils P. How unique and traceable are usernames. International Symposium on Privacy Enhancing Technologies Symposium. 2011;1–17.

2. Bartunov S., Korshunov A. Identification of users of social networks on the Internet based on social connections. Trudy Instituta sistemnogo programmirovaniya Rossiyskoy akademii nauk = Proceedings of the Institute of System Programming of the Russian Academy of Sciences. 2012;14(2):1–13. (In Russ.)

3. Lapenok M.V., Patrusheva O.M. User identification in various social networks by means of analyzing the user's social connections and profile attributes. Obrazovatelʹnyye tekhnologii i obshchestvo = Educational technologies and society. 2016;19(3):584–594. (In Russ.)

4. Korshunov A., Beloborodov I., Buzun N., Avanesov V., Pastukhov R., Chikhradze K., Kozlov I., Gomzin A., Andrianov I., Sysoev A., Ipatov S., Filonenko I., Chuprina K., Turdakov D., Kuznetsov S. Analysis of social networks: methods and applications. Trudy Instituta sistemnogo programmirovaniya Rossiyskoy akademii nauk = Proceedings of the Institute of System Programming of the Russian Academy of Sciences. 2014;26(1):439–456. (In Russ.)

5. Ling Xing, Kaikai Deng, Honghai Wu и др. A Survey of Across Social Networks User Identification. IEEE Access. 2019;7:137472–137488.

6. Karen S.J. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation. 2004;60(5):493–502.

7. Gaidamakin N.A. Measure of similarity of sequences of the same dimension. Matematicheskiye struktury i modelirovaniye = Mathematical structures and modeling, 2016;4(40):5–16. (In Russ.)

8. Korepanova A.A., Oliseenko V.D., Abramov M.V., Tulupyev A.L. Application of machine learning methods in the task of identifying user accounts in two social networks. Kompʹyuternyye instrumenty v obrazovanii = Computer Tools in Education. 2019;3:29–43. (In Russ.)

9. Leontiev V.K. About similarity measures and distances between objects. Zhurnal vychislitelʹnoy matematiki i matematicheskoy fiziki = Journal of Computational Mathematics and Mathematical Physics. 2009;49(11):2041–2058. (In Russ.)

10. Ponizovkin D.M. The influence of similarity measures on the effectiveness of RS. Programmnyye sistemy: teoriya i prilozheniya = Software systems: theory and applications. 2014;5(23):55–65. (In Russ.)

Romanov Alexander Georgievich

Academy of Management of the Ministry of Internal Affairs of Russia

Moscow, Russian Federation

Keywords: user identification, internet, data analysis, social networks, crimes

For citation: Romanov A.G. Methods of user identification in the information and telecommunication environment based on the analysis of account attributes. Modeling, Optimization and Information Technology. 2022;10(3). Available from: https://moitvivt.ru/ru/journal/pdf?id=1203 DOI: 10.26102/2310-6018/2022.38.3.002 (In Russ).

42

Full text in PDF

Received 15.06.2022

Revised 29.06.2022

Accepted 15.07.2022

Published 18.07.2022