Volume - 6 | Issue - 4 | december 2024
Published
11 January, 2025
In E-Learning Platforms like LinkedIn, identifying fake profiles poses significant challenges, affecting trust and engagement. Fake Profiles can mislead users, dilute the quality of interactions and undermine the credibility of the Platform. This survey explores the application of machine learning techniques for detecting fake profiles on e-learning platforms. The study examines various features that can indicate suspicious behaviours, such as anomalous login patterns, unusual interaction metrics, and inconsistent profile information. Through a comprehensive review of existing research, the study identifies the key challenges in implementing machine learning solutions for fake profile detection, such as data privacy concerns, feature engineering, and scalability. Additionally, the survey highlights the potential of using machine learning models and ensemble techniques to enhance detection accuracy. By consolidating insights from prior studies, this survey aims to provide a foundation for future research and development in safeguarding e-learning platforms against fake profiles, thereby enhancing a secure and trustworthy digital learning environment.
KeywordsFake Profiles E-Learning Platforms Machine Learning User Behaviour Fraud Detection
