A. Mahendhar, M. Vamshikrishna, Paila Arjun, V. Abhinay, Ajith Kumar

DOI: https://doi.org/10.5281/zenodo.23032209

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.

Cybersecurity, Machine Learning, Phishing Detection, Random Forest, Feature Extraction, URL Classification, Web Security, Threat Intelligence, Flask, Software Requirements Specification.

This paper presented the design, architecture, and implementation of an AI-Based Cybercrime Detection and Prevention System that applies a Random Forest classifier to real-time URL analysis within a modular, Flask-based web architecture. The system automates threat detection, reduces reliance on manual analyst review, and exposes its findings through a user-facing prediction interface and an administrator monitoring dashboard, addressing the SRS requirements for performance, security, usability, and scalability derived in Section III. A review of twenty-five related studies situates the design choices—an ensemble tree-based classifier for interpretable, real-time classification, and a layered SOC-style dashboard architecture—within the broader cybersecurity machine learning literature. The layered architecture, feature extraction pipeline, and Agile development process described in Sections IV and V translate these design choices into a working prototype whose implementation stack, database schema, and security controls are documented in Section VI. The resulting prototype shows that a lightweight, ensemble-learning approach can deliver real-time phishing and malicious-URL detection without the computational overhead associated with deep-learning alternatives, while remaining extensible to additional cyber-threat categories. Taken together with the limitations discussed in Section VII, the work offers both a practical reference implementation and a structured requirements baseline for future extensions of AI-based cybercrime-detection tooling.

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A. Mahendhar, M. Vamshikrishna, Paila Arjun, V. Abhinay, Ajith Kumar (2026). AI-Based Cybercrime Detection and Prevention System. International Journal of Computer Science Engineering Techniques, 10(5), 182–190. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.23032209