AI-Powered Cybercrime Reporting System

Abstract

With the exponential increase in digital threats, the traditional cybercrime reporting process remains largely unstructured and inaccessible to common users.This article proposes an AI-powered cybercrime reporting system that takes advantage of natural language processing and machine learning to offer an intelligent, guided interface for victims to report incidents.The system employs a fine-tuned RoBERTa-base model to classify cybercrimes into 23 predefined categories based on user descriptions and dynamically adjusts the reporting flow to collect appropriate data. Additionally, it enables secure digital evidence handling and automated PDF report generation for law enforcement. The proposed system improves reporting accuracy, user confidence, and evidence completeness, representing a transformative change in digital law enforcement support tools.

Introduction

Cybercrime reporting faces significant challenges due to unclear traditional systems, inconsistent definitions, and limited technical familiarity among users. Victims often struggle to file organized reports, and law enforcement lacks adequate training, making investigations difficult.

AI technologies, especially advanced models like RoBERTa-base, offer solutions by enabling victims to report crimes in natural language while guiding them through relevant questions. This improves crime classification, automates evidence collection, structures reports, and secures sensitive data with role-based access. Such AI-driven systems enhance communication between victims and authorities, streamline investigations, and improve response efficiency.

The literature highlights AI’s role in cybercrime detection, anomaly detection, natural language processing for threat intelligence, and AI-powered security management. However, challenges remain, including AI misuse by attackers, data quality issues, and the need for explainable AI. Current reporting platforms rely heavily on manual input and lack guidance or real-time feedback, limiting effectiveness.

The proposed AI-powered cybercrime reporting system uses a fine-tuned RoBERTa-base model trained on 23 cybercrime types. It features dynamic questioning to engage users, an evidence management system that complies with forensic standards, automated legal report generation, and user safety advice. The system architecture combines modern web technologies and secure backend frameworks, supported by an administrator dashboard to assist law enforcement in managing and analyzing reports effectively.

Conclusion

The study introduces a new system for reporting cybercrime that uses AI technology to make the process better. Traditional methods have several issues, but this system addresses them by combining smart classification, dynamic data collection, and secure evidence handling into one easy-to-use platform. The system uses a specially trained RoBERTa-base model to understand users\' natural language and accurately classify different cybercrimes. It includes a smart questioning tool and can generate structured PDF reports, ensuring that every report contains all necessary details and is immediately helpful for police. Tests show that this system greatly improves accuracy, speed, and user satisfaction. It is designed to grow and adapt, making it an excellent solution for reporting digital crimes in today\'s world. This system effectively bridges the gap between victims and investigators, helping them work together more efficiently in our evolving digital landscape.

References

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Copyright

Copyright © 2025 Sabitha K, Jeyaprabakaran S, Vishwanatha Sriram M, Albin Tenny, Sasidharan S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.