Байесовские методы в анализе противоправной активности пользователей электронных торговых площадок
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Bayesian methods in the analysis of illegal activity of users of electronic trading platforms

Romanov A.G.  

UDC 004.931
DOI: 10.26102/2310-6018/2020.30.3.024

  • Abstract
  • List of references
  • About authors

The paper considers the issues of prevention and detection of crimes committed in the information and communication environment, as well as its use. Given the increasing demand for the Internet as an important social component in the state's development strategy, the development and implementation of tools, preventive measures and methods for solving crimes committed in the virtual environment in the system of law enforcement cannot be overestimated. Despite the fact that algorithms for committing crimes of this type are widely known and well-studied by domestic and foreign authors, methods for solving such crimes and questions of their practical application remain a topical subject of scientific research. This article discusses a possible mechanism for law enforcement agencies based on a preliminary study and identification of patterns in the use of the Internet by its users. Based on data mining methods, we consider ways to improve the effectiveness of internal Affairs agencies in the application of measures to prevent and solve crimes in the information and communication environment. The method proposed in this paper provides an opportunity to forecast demand and supply for commercial offers posted on the global network that are associated with criminal manifestations. The use of these scenarios in law enforcement provides an opportunity not only to organize preventive measures to prevent the onset of criminal consequences, but also to disclose previously committed criminal acts.

1. Kharisova Z. I. Actual problems of law enforcement agencies ' activity to counteract crime in the global Internet network. Bulletin of the Ufa law Institute of the Ministry of internal Affairs of Russia. 2019;3(85):92-98.

2. Suleymanova I. E. Cybercrime and youth: a modern view on solving the problem. Bulletin of the all-Russian Institute for advanced training of employees of the Ministry of internal Affairs of the Russian Federation. 2018;2(46):96-99.

3. A comprehensive study of the problem of cybercrime. Official Internet portal of the United Nations office on drugs and crime. Available at: https://www.unodc.org/documents/organizedcrime/UNODC_CCPCJ_EG.4_2013/CYBERCRIME_STUDY_210213.pdf (date accessed: 20.06.2020)

4. Informational and analytical portal of legal statistics of the Prosecutor General's office of the Russian Federation. Available at: http://crimestat.ru/analytics (date accessed: 18.06.2020)

5. Piatetsky-Shapiro, G. Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases. AAAI/MIT Press, Cambridge. 1991;248:255-264.

6. Agrawal, Rakesh & Imielinski, Tomasz & Swami, Arun. 1993. Mining Association Rules Between Sets of Items in Large Databases, SIGMOD Conference. 10.1145:170036-170072.

7. Agrawal, Rakesh & Srikant, Ramakrishnan. Fast Algorithms for Mining Association Rules. Proc. 20th Int. Conf. Very Large Data Bases. VLDB. 2000;1215:144-156.

8. G. Sılahtaroğlu and H. Dönertaşli, «Analysis and prediction of Ε-customers' behavior by mining clickstream data» 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015:1466-1472, doi: 10.1109/BigData.2015.7363908.G. Sılahtaroğlu and H. Dönertaşli, «Analysis and prediction of Ε-customers' behavior by mining clickstream data» 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015:1466-1472, doi: 10.1109/BigData.2015.7363908.

9. Rodmorn, Chonnikarn & Panmuang, Mathuros & Potiwara, Khuanwara. Analysis of the internet using behavior of adolescents by using data mining technique. 2015:398-402. doi:10.1109/ICITEED.2015.7408979.

10. Zhao, Chunye & Tu, Shanshan & Chen, Haoyu & Huang, Yongfeng. Efficient association rule mining algorithm based on user behavior for cloud security auditing. 2016:145-149. doi:10.1109/ICOACS.2016.7563067.

11. Ji, Junzhong & Zheng, Lei & Liu, Chunnian. The Intelligent Electronic Shopping System Based on Bayesian Customer Modeling. 2001:574-578. doi:10.1007/3-540-45490-X_74.

12. De Bruyn, Arnaud & Otter, Thomas. 2016. Bayesian Customer Profiling: Applications to Age and Political Partisanship Estimation. SSRN Electronic Journal. doi:10.2139/ssrn.2740293.

13. Yang, Qin & Li, Zhirui & Jiao, Haisen & Zhang, Zufang & Chang, Weijie & Wei, Daozhu. (2019). Bayesian Network Approach to Customer Requirements to Customized Product Model. Discrete Dynamics in Nature and Society. 2019. 1-16. doi:10.1155/2019/9687236.

14. Chen, Kejia & Jin, Jian & Zhao, Zheng & Ji, Ping. 2020. Understanding customer regional differences from online opinions: a hierarchical Bayesian approach. Electronic Commerce Research. doi:10.1007/s10660-020-09420-5.

15. M. E. Burlakov Application of the optimized naive Bayesian classifier in the problem of classification of SMS messages. Proceedings of the Samara scientific center of the Russian Academy of Sciences. 2016;4(4);705-709.

16. V. L. Chernyshev, A. A. Tolchennikov Properties of distribution of Gaussian packets on a spatial network. Science and education. 2011;10;1-10.

17. Nilesh B. Madke et. al. User Profile Based Behavior Identification Using Data Mining Technique / International Research Journal of Engineering and Technology(IRJET) 2018;5:326-331.

Romanov Alexander Georgievich

Email: psychology.crimea@gmail.com

Academy Of Management Of The Ministry Of Internal Affairs Of Russia

Moscow, Russian Federation

Keywords: data mining, internet, crime, forecasting, electronic commerce, a posteriori probability

For citation: Romanov A.G. Bayesian methods in the analysis of illegal activity of users of electronic trading platforms. Modeling, Optimization and Information Technology. 2020;8(3). Available from: https://moit.vivt.ru/wp-content/uploads/2020/08/Romanov_3_20_1.pdf DOI: 10.26102/2310-6018/2020.30.3.024 (In Russ).

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