Methods for Detecting Fake Profiles in Online Social Networks Using Artificial Neural Networks

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Published: Apr 30, 2020
DOI: https://doi.org/10.17762/turcomat.v11i1.14272
Keywords:
Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Naive Bayes, Decision Tree (SVM)

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Dr. Koparthi Suresh
Professor, Bhimavaram Institute of Engineering & Technology, Pennada, Bhimavaram, India
Dr. RVVSV Prasad
Professor, Swarnandhra College of Engineering & Technology, Narsapur, - 534275, India

Abstract

In light of the current situation, most individuals are participating in online social networks. Everyone, from kids to adults, spends a lot of time on these sites, either learning new things or getting in touch with friends and family in more effective ways. However, today's social media platforms are plagued by a large number of bogus accounts that exploit security flaws in order to steal from the sites or commit cybercrimes themselves.

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How to Cite
Suresh, D. K. ., & Prasad, D. R. (2020). Methods for Detecting Fake Profiles in Online Social Networks Using Artificial Neural Networks. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(1), 991–997. https://doi.org/10.17762/turcomat.v11i1.14272
Issue
Vol. 11 No. 1 (2020)
Section
Research Articles
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References

. Awasthi, S., Shanmugam, R., Jena, S.R. and Srivastava, A., 2020. Review of Techniques to

Prevent Fake Accounts on Social Media.

. Hajdu, G., Minoso, Y., Lopez, R., Acosta, M. and Elleithy, A., 2019, May. Use of Artificial

Neural Networks to Identify Fake Profiles. In 2019 IEEE Long Island Systems, Applications and

Technology Conference (LISAT) (pp. 1-4). IEEE.

. Kaur, J. and Sabharwal, M., 2018. Spam detection in online social networks using feed forward

neural network. In RSRI conference on recent trends in science and engineering (Vol. 2, pp. 69-78).