Blockchain Enabled Social Network for Detecting Fake Accounts

Authors

  • B. Deepika Assistant Professor, Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Maharashtra, India Author
  • S. Sneka Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Maharashtra, India Author
  • S. Susila Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Maharashtra, India Author
  • P. Suvetha Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Maharashtra, India Author
  • S. Swetha Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Maharashtra, India Author

DOI:

https://doi.org/10.32628/CSEIT24102107

Keywords:

Social Network Framework, Account Creation, Profile Similarity, Aadhar Verification, Fake Account Classification, Support Vector Machine, Access Social Network

Abstract

Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.

Downloads

Download data is not yet available.

References

Cola, Guglielmo, Michele Mazza, and Maurizio Tesconi. "Twitter Newcomers: Uncovering the Behavior and Fate of New Accounts through Early Detection and Monitoring." IEEE Access (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3282580

Averza, Aldo, Khaled Slhoub, and Siddhartha Bhattacharyya. "Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation." IEEE Access 10 (2022): 129394-129407. DOI: https://doi.org/10.1109/ACCESS.2022.3228258

Kantartopoulos, Panagiotis, Nikolaos Pitropakis, Alexios Mylonas, and Nicolas Kylilis. "Exploring adversarial attacks and defences for fake twitter account detection." Technologies 8, no. 4 (2020): 64. DOI: https://doi.org/10.3390/technologies8040064

López-Vizcaíno, Manuel F., Francisco J. Novoa, Diego Fernández, and Fidel Cacheda. "Measuring Early Detection of Anomalies." IEEE Access 10 (2022): 127695-127707. DOI: https://doi.org/10.1109/ACCESS.2022.3224467

Zhu, Xinjie, Debiao He, Zijian Bao, Min Luo, and Cong Peng. "An efficient decentralized identity management system based on range proof for social networks." IEEE Open Journal of the Computer Society 4 (2023): 84-96. DOI: https://doi.org/10.1109/OJCS.2023.3258188

Sansonetti, Giuseppe, Fabio Gasparetti, Giuseppe D’aniello, and Alessandro Micarelli. "Unreliable users detection in social media: Deep learning techniques for automatic detection." IEEE Access 8 (2020): 213154-213167. DOI: https://doi.org/10.1109/ACCESS.2020.3040604

Bhambar, Snehal, Kanchan Khairnar, Yogita Nikam, Harshali Shelar, and Y. K. Desai. "DETECTING FAKE ACCOUNTS ON SOCIAL MEDIA USING NEURAL NETWORK." International Research Journal of Modernization in Engineering Technology and Science 4, no. 5 (2022).

Lei, Licai, and Enyu Zeng. "Research on the relationship between perceived social support and exercise behavior of user in social network." IEEE Access 8 (2020): 75630-75645. DOI: https://doi.org/10.1109/ACCESS.2020.2987073

Xia, Tianyu, Yijun Gu, and Dechun Yin. "Research on the link prediction model of dynamic multiplex social network based on improved graph representation learning." IEEE Access 9 (2020): 412-420. DOI: https://doi.org/10.1109/ACCESS.2020.3046526

Wang, Yu, and Qilong Zhao. "Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System." IEEE Access 10 (2022): 87639-87649. DOI: https://doi.org/10.1109/ACCESS.2022.3199364

Downloads

Published

27-04-2024

Issue

Section

Research Articles

How to Cite

[1]
B. Deepika, S. Sneka, S. Susila, P. Suvetha, and S. Swetha, “Blockchain Enabled Social Network for Detecting Fake Accounts”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 747–756, Apr. 2024, doi: 10.32628/CSEIT24102107.

Similar Articles

1-10 of 274

You may also start an advanced similarity search for this article.