Fake profile detection using Artificial Neural Network (ANN) / Nik Nor Asiah Zakaria
In the current generation, everyone's social life is now linked with online social networks. These websites have had a significant impact on how we conduct our social life. It's lot simpler to make new acquaintances and keep in touch with them now. But because of their quick development, m...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/96386/1/96386.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In the current generation, everyone's social life is now linked with online social networks. These websites have had a significant impact on how we conduct our social life. It's lot simpler to make new acquaintances and keep in touch with them now. But because of their quick development, many fresh problems have surfaced, such as malicious individuals, fake profiles, and online impersonation. In this study, we employ an artificial neural network to accurately and automatically identify false profiles. The objective of this system is to investigate the need for Artificial Neural Networks (ANN) in fake profile detection, develop a detection system for fake profiles using ANN, and evaluate the performance of ANN in a fake profile detection system. The dataset used in the study is an existing dataset from GitHub, specifically Fake Instagram Profile Detection using ANN. The training dataset consists of 80%, 556 data of real and fake profiles, while the testing dataset consists of 20%, 140 data of real and fake profiles. We assess the likelihood that a friend request on Instagram is genuine or not. Online social networks that have millions of profiles that can't be manually verified can make use of this. The study can help society detect fake profiles at an early stage and assist them in taking appropriate steps benefiting society from scam activities using fake profiles as well as reducing the number of cybercrime victims which can potentially leading to financial losses. |
|---|