Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

Automating Dark Web Content Analysis with CNNs and Topic-Based Feature Selection

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  • S Sagarika Reddy,
  • S Vishnu Vardhan Kumar,
  • S Reddy Kiran,
  • S Sada Siva Reddy,
  • Chaitanya P
S Sagarika Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Vishnu Vardhan Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Reddy Kiran
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Sada Siva Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
Chaitanya P
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15186931

Published 2025-04-10

Keywords

  • Dark Web,
  • text classification,
  • deep learning,
  • TextCNN,
  • topic modeling,
  • cyber threat detection
    • ...More
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How to Cite

S Sagarika Reddy, S Vishnu Vardhan Kumar, S Reddy Kiran, S Sada Siva Reddy, & Chaitanya P. (2025). Automating Dark Web Content Analysis with CNNs and Topic-Based Feature Selection. International Journal of Computational Learning & Intelligence, 4(2), 473–483. https://doi.org/10.5281/zenodo.15186931

Copyright (c) 2025 S Sagarika Reddy, S Vishnu Vardhan Kumar, S Reddy Kiran, S Sada Siva Reddy, Chaitanya P

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The Dark Web is a segment of the internet designed to provide user anonymity, making it a hub for illicit activities and a repository of cyber threats. Due to the challenges in monitoring and classifying Dark Web content, this study proposes a novel classification method that integrates TextCNN and topic modelling weights to enhance accuracy in identifying malicious activities. Traditional methods rely on processing entire Dark Web texts, which often include irrelevant content, reducing classification efficiency. To optimize performance, this research focuses on extracting key terms for each category, reducing the dimensional complexity of word vectors. Topic modeling weights are applied to refine feature selection, ensuring that only highly relevant terms contribute to classification. By incorporating TextCNN, the model achieves improved precision and computational efficiency. The approach was validated using two Dark Web datasets, demonstrating superior classification performance compared to conventional text classification techniques. The findings suggest that integrating topic modeling weights with deep learning significantly enhances the ability to classify Dark Web content accurately while maintaining computational efficiency. This methodology presents a scalable solution for cybersecurity applications, enabling more effective threat detection and analysis on the Dark Web.
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