AI-Based Cybercrime Detection and Prevention System

Abstract

digital services have expanded rapidly, and cybercrime has become one of the most pressing threats to secure online interaction. Traditional signature- and rule-based security tools are largely reactive, so they struggle to keep pace with newly evolving attacks such as phishing, malicious URL distribution, and identity theft. This paper presents an AI-Based Cybercrime Detection and Prevention System that uses supervised machine learning to classify URLs and user-interaction patterns as safe or malicious in real time. The system extracts lexical and host-based features from each submitted URL and applies a Random Forest classifier to carry out automated threat classification, and it exposes this functionality through a Flask-based web dashboard for end users and administrators. The paper covers the software requirements specification, the layered system architecture, the Agile-based development methodology, and the implementation stack (Python, Flask, HTML/CSS/JavaScript, and a relational/NoSQL database) used to build the system. A review of twenty-five related studies is used to position the proposed design against existing phishing-detection, intrusion-detection, and explainable-AI literature. The results suggest that ensemble tree-based classifiers such as Random Forest strike a favourable balance between detection accuracy, interpretability, and computational cost for real-time deployment, while the accompanying dashboard automates threat logging and reduces the manual effort required of security analysts. The paper closes with a discussion of current limitations and directions for future work, including deep-learning integration, threat-intelligence API feeds, and federated, privacy-preserving training.