The approach uses a crawler to extract metadata and combines security-focused analysis with machine learning–based classification to identify harmful activity and present the factors contributing to detection results in a clear and understandable manner. The project aims to support efficient security decision-making by focusing on the presentation and interpretation of classification outcomes alongside detection accuracy. It aligns with current academic and industry objectives and addresses a relevant and active challenge in cybersecurity by balancing technical detection capabilities with practical analysis and application.