The enhancement of online social network security by detecting and preventing fake accounts through machine learning

Document Type : Original Article

Authors

1 Assistant Professor, Computer Department, Tehran Payam Noor University, Tehran, Iran

2 Master's degree, Department of Information Technology, Tehran Payam Noor University, Tehran, Iran

3 Assistant Professor, Computer and Information Technology Department, Tehran Payam Noor University, Tehran, Iran

Abstract

Today, with the pervasiveness of social networks, the security of this environment is considered as one of the most important network issues. One of the security challenges is creating fake accounts that harass social media users. The owners of these fake accounts pursue goals such as creating likes and followers or distributing misinformation for political, cultural and economic purposes. In this study, with the aim of improving security in social networks and improving the security of cyberspace, a method for investigating and detecting fake accounts is presented. This method proposes an algorithm that combines the decision tree, the nearest neighbor and Bayes methods. The results of this combined algorithm demonstrate an accuracy of 95.34%. This method has stability and does not suffer from overfit, as proved in the conclusion. The results of this research can be used to provide solutions to prevent the creation of fake accounts and increase account security and lead to the recognition and use of new data mining techniques and also data analysis in social networks. One of the achievements of this research is the method of detecting the falsity of the account and identifying the factors affecting its detection, which has been done using a hybrid algorithm that obtains correct results.

Keywords

Main Subjects

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References

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Electronic and Cyber Defense
Volume 10, Issue 1 - Serial Number 37
Serial No. 37, Spring Quarterly
May 2022
Pages 85-97

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History

  • Receive Date: 19 May 2021
  • Revise Date: 10 July 2021
  • Accept Date: 13 December 2021
  • Publish Date: 22 May 2022

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