Abstract: The rapid growth of social media platforms has given rise to new opportunities for communication, networking, and information sharing across the globe, but it has also given rise to fraudulent and spoof accounts that compromise user trust, spread misinformation, and facilitate malicious activities. Detecting such accounts accurately is challenging due to their dynamic behavior and the complexity of social interactions online. This project presents an automated A framework that makes use of machine learning techniques to analyze data and generate intelligent prediction.to identify fraudulent and spoof accounts in social media. The system integrates multiple models, including XG Boost, Random Forest, and Naïve Bayes, to analyze account features and behavioral patterns effectively. A web-based platform has been developed to provide real-time detection, user authentication, and historical logging for enhanced usability and security. The framework was tested on standard datasets, and the results demonstrate high accuracy in identifying and separating authentic users from fraudulent or impersonated accounts. The study highlights the suggested approach ensures that the system is more efficient and reliable, solution is both scalable and adaptable, making it a reliable approach for strengthening security in social.

Keywords: Cybersecurity, Spam Filtering, Machine Learning, Random Forest, XG Boost, Naïve Bayes, Hybrid Modeling.

Downloads: | DOI: 10.17148/IARJSET.2025.12822