Automatic Detection of Fake Profiles in Online Social Networks

Authors

  • RV Kotawadekar Department of MCA, Finolex Academy of Management and Technology, Maharashtra, India
  • AS Kamble Department of MCA, Finolex Academy of Management and Technology, Maharashtra, India
  • SA Surve Department of MCA, Finolex Academy of Management and Technology, Maharashtra, India

DOI:

https://doi.org/10.26438/ijcse/v7i7.4045

Keywords:

Threats, Facebook Immune System, Classification, Training Datasets, Profile Attributes

Abstract

In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Naive Bayes and Decision trees to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles cannot be examined manually.

References

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Published

2019-07-31
CITATION
DOI: 10.26438/ijcse/v7i7.4045
Published: 2019-07-31

How to Cite

[1]
K. RV, K. AS, and S. SA, “Automatic Detection of Fake Profiles in Online Social Networks”, Int. J. Comp. Sci. Eng., vol. 7, no. 7, pp. 40–45, Jul. 2019.

Issue

Vol. 7 No. 7 (2019): IJCSE July Edition

Section

Research Article

License

Creative Commons License

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