An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6650
Keywords:
Cyber security, Scammer profiles, Online social network frauds, Scammer detection model, Social threats, Compromised accounts, Fake profiles, Dating Fraud, Machine Learning

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Smita Bharne
School of Computer Sciences and Engineering, Sandip University/ Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University Nashik, India
Pawan Bhaladhare
School of Computer Sciences and Engineering Sandip University Nashik, India

Abstract

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety.

Article Details

How to Cite
Bharne, S. ., & Bhaladhare, P. . (2023). An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 239–249. https://doi.org/10.17762/ijritcc.v11i5s.6650
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

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