Analysis of exchange market disruptors using graph-based social network analysis

Document Type : Original Article

Author

Assistant Professor, FATA Department, Amin University of Police Sciences, Tehran, Iran

Abstract

Today, increasing the science and technology and the communication technologies, especially in cyberspace, however physically act have become interact with cyberspace has caused a more significant effect on the culture and geography of each country. Accordingly, dealing with these physical crimes interacts with cyberspace. Therefore, detecting crimes and identifying criminals using old methods is almost impossible. Therefore, databases and their processing can play an essential role in detecting crime patterns for police-security organizations. The highly effective methods and tools of social network analysis can discover the pattern and extract knowledge from the database to prevent and control crime. This article explores crime rules using social network analysis methods and offers suggestions for preventing crimes and identifying perpetrators. The analysis of social networks has great importance, and the results obtained from these analyzes can be used in similar applications. In this article, the first has been collected the data related to currency disruptors in recent years, then analyzed this data with social network techniques and identified compelling features for identifying virtual nodes. The results show that social network analysis methods have simulated a model with acceptable accuracy and introduced destructive nodes by analyzing features. However, identifying destructive nodes and crime prevention can be considered, thoroughly describing how to do this in the paper.

Keywords

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References

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Electronic and Cyber Defense
Volume 11, Issue 1 - Serial Number 41
No. 41, Spring
May 2023
Pages 107-116

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History

  • Receive Date: 07 May 2022
  • Revise Date: 14 July 2022
  • Accept Date: 24 December 2022
  • Publish Date: 22 May 2023

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