Dark Side Of The Web: Dark Web Classification Based On Textcnn & Topic Modeling Weight

Abstract

The dark web has emerged as a significant platform for illegal activities such as cybercrime, drug trafficking,
terrorism financing, and data trading. Monitoring and analyzing dark web content is challenging due to its
unstructured nature, anonymity, and rapidly evolving language patterns. Traditional keyword-based and ruledriven
approaches fail to capture semantic meaning and hidden contextual relationships within dark web text.
This paper proposes a hybrid text analytics system for dark web monitoring that combines Convolutional
Neural Network (CNN)-based feature extraction with topic modeling techniques. CNNs are employed to
automatically learn discriminative textual features, while topic modeling uncovers latent thematic structures
within the content. The integration of deep learning and probabilistic topic modeling enhances classification
accuracy, improves interpretability, and enables effective monitoring of dark web activities. Experimental
evaluation shows that the hybrid approach outperforms conventional text analysis methods, making it suitable
for proactive cyber threat intelligence and law enforcement applications.