Intelligent Detection of Malicious Websites Using Machine Learning

Abstract

Social media platforms have become an essential medium for communication, business, and information sharing. However, the increasing growth of these platforms has also given rise to fake accounts that pose threats such as misinformation, cyber fraud, spam, and identity impersonation. Detecting and preventing fake accounts is therefore a critical challenge to ensure the security, reliability, and trustworthiness of online communities. This project focuses on the detection of fake accounts on social media using machine learning techniques. By analyzing user profile features, activity patterns, content characteristics, and network behavior, the system can differentiate between genuine and fake users. The study explores both traditional rule-based methods and modern algorithms such as Decision Trees, Random Forests, and XGBoost, which improve detection accuracy. The proposed approach emphasizes building an intelligent model that learns from realworld datasets and adapts to evolving patterns of fake account creation. The outcome of this project will help in reducing the spread of spam, safeguarding users from fraudulent activities, and enhancing the overall trust of social media platforms. This work not only contributes to the technical understanding of fake account detection but also highlights the importance of ethical considerations, user privacy, and scalability in real-world applications. Keywords—Malicious websites, Machine learning, Cyber security, URL analysis, Network traffic.