Unmasking Fake Profiles – Machine Learning in Social Network Security

Unmasking Fake Profiles - Machine Learning in Social Network Security

L.Sankara Rao1, S.Manideep2*, S.Dally3 , M.V.N.Sai Saketh4, T.Rohit Rao5

1Asst.Professor, Dept of CSE AI-ML, Raghu Institute of Technology

2*Student, Dept of CSE AI-ML, Raghu Institute of Technology

3Student, Dept of CSE AI-ML, Raghu Institute of Technology

4Student, Dept of CSE AI-ML, Raghu Institute of Technology

5student, Dept of CSE AI-ML, Raghu Institute of Technology

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Abstract - The proliferation of fake profiles in social media poses significant threats to online security, privacy, and trust. Existing methods for detecting fake profiles rely heavily on manual verification, rule-based systems, or shallow machine-learning models, which are often ineffective against sophisticated fake profiles. This study proposes a novel machine learning framework for unmasking fake profiles in social media. Our approach leverages a combination of Extracting comprehensive features from user profiles, including behavioral, network, and content-based attributes, Data augmentation and Ensemble learning. Traditional machine-learning methods have limitations in detecting fake human accounts on social media. These accounts often mimic real users, making them difficult to distinguish. To overcome this challenge, we propose a new model. This new model can have more accuracy than the previous methods. By training the model on a dataset of real and fake accounts, we can improve the accuracy of detecting fake human accounts and enhance the security of social media platforms.

Key Words: Fake Profiles, Social Media, Instagram, Machine Learning(ML).