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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM17296
Volume:
6 |
12 |
Dec 2022
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The unconstrained increase in online social media usage has resulted in unchecked growth in the number of user profiles that are fake and causes a lot of damage to social media networks. This results in a dreadful threat posed to internet security. It is extremely important to identify the fake profiles causing the threat. These user accounts often commit fraud like retrieving personal data and confidential information from the victim accounts, spreading false information about the users online, and threatening and bullying the victims all by making sure that their identities are not revealed to the public. These fraud accounts look realistic but follow some patterns which help identify them. This project aims to help the users identify the difference between genuine users and fake frauds on Facebook. This project uses the concepts of machine Learning particularly Neural networks to identify if the Facebook friend request is genuine or fake. Finally, the solution is found by considering all the important parameters involved in the social network page.
KEYWORDS
Publication Date
12/24/2022
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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