Dark web text classification using BERT's Language Model

Document Type : Original Article

Authors

1 Master's student,Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

2 Assistant Professor, Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

The hidden nature and limited access of the dark web has led to the proliferation of many criminal activities, including cyber threats, arms sales, drug sales, and the sale of illegal tools. The emergence of large language models has created the hope that it will be possible to analyze the content on the dark web with proper accuracy. In this regard, the use of mass cyber data available in the dark web will be very useful and effective to prevent cyber threats and train language models. The technology of large language models requires a lot of high-quality data for better training and to achieve sufficient accuracy, and this is the challenge that researchers in the field of cyber security face due to the contamination of the data available on the dark web. Most of the researches in this field have been focused on all the characteristics of the dark web dataset and low-quality data and have not been able to achieve high accuracy. In this thesis, we presented a new language model based on the BERT-based language model, which was trained on the data extracted from the dark web. The proposed model is a transformer-based text model that uses a two-way encoder of transformers for a learning approach and we evaluated it on a high - quality dataset, without repetitive data, free of unknown words, all in English and specifically on hacking and security data. Finally, by analyzing the evaluated values of the proposed model with the previous models, it was found that the proposed model was able to have better accuracy in data classification due to the injection of quality data compared to the previous models.

Keywords

Main Subjects

Files

History

  • Receive Date: 19 September 2025
  • Revise Date: 08 November 2025
  • Accept Date: 28 November 2025
  • Publish Date: 22 December 2025

Share

How to cite

Statistics