Unmasking Fake Profiles – Machine Learning in Social Network Security

– 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.