Behavior Prediction and Anomaly Detection: Leveraging ML for Online Child Safety

The rapid increase in harmful content online poses significant risks to child safety, emphasizing the urgent need for effective detection and prevention systems. This study explores the use of advanced machine learning (ML) techniques to tackle these challenges by integrating natural language processing (NLP) and computer vision models. Unlike traditional approaches, our methodology emphasizes the real-time analysis of both textual and multimedia content to detect harmful behaviors such as cyberbullying, grooming, and exposure to inappropriate material. By leveraging models like GPT-Neo, LightGBM, XGBoost, and Random Forest, this work achieves high accuracy in anomaly detection and content moderation. Additionally, the study addresses critical issues such as the scarcity of annotated datasets, language diversity, and the computational demands of real-world deployment. The findings underscore the potential of ML-driven solutions to create safer digital environments for children by providing comprehensive, scalable, and efficient protection systems.