Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts

TL;DR

This paper presents a transformer-based framework for automatically classifying multilingual, code-mixed cybercrime complaints in Hinglish, improving accuracy and scalability for real-world law enforcement applications.

Contribution

It introduces Hinglish-adapted transformers and a data augmentation approach to effectively classify cybercrime complaints, addressing language and privacy challenges.

Findings

HingRoBERTa achieved 74.41% accuracy

The framework effectively handles code-mixed Hinglish texts

The solution is scalable and deployable in real-world platforms

Abstract

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and traditional machine learning methods fail to capture the semantic and contextual nuances of textual cybercrime complaints. Moreover, the lack of publicly available datasets and privacy concerns hinder the research to present robust solutions. To address these challenges, we propose a framework for automated cybercrime complaint classification. The framework leverages Hinglish-adapted transformers, such as HingBERT and HingRoBERTa, to handle code-mixed inputs effectively. We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024. We employ GenAI open source model-based data augmentation method to address class imbalance. We also employ privacy-aware preprocessing to ensure compliance with ethical standards while maintaining data integrity. Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%. We also develop ready-to-use tool by integrating Django REST backend with a modern frontend. The developed tool is scalable and ready for real-world deployment in platforms like the National Cyber Crime Reporting Portal. This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.