The dark web is the canopy idea that specifies any sort of illegal actions conducted by unknown organizations or persons, thus making it complicated to track. The illegal contents on the dark web are changed and updated constantly. The classification and collection of such activities are highly challenging tasks, since they are time-consuming and difficult. In present times, it has emerged as a problem that needs rapid attention from academia and industry. In this research, a Bayesian Hierarchical Neural Attention Harmonic Network (BHNAHN) is presented for dark web classification. Here, dark web crawling and classification of the dark web are the two steps conducted. TorBot is employed for dark web crawling based upon keywords like pornography, financial gambling, drugs, hacking, cryptocurrency, arms/weapons, electronics and violence. In the dark web classification process, input web data is acquired and then, Bidirectional Encoder Representations from Transformers (BERT) tokenization is carried out. Afterwards, features are extracted from tokenized words. Finally, dark web classification is accomplished employing BHNAHN. BHNAHN is modeled by incorporating a Bayesian Neural Network (BNN) and Hierarchical Neural Attention classifier with forward harmonic analysis. Additionally, BHNAHN obtained maximal accuracy, True Negative Rate (TNR) and True Positive Rate (TPR) of about 91.362%, 92.440% and 90.799% respectively.
Keywords: Dark web, Bidirectional Encoder Representations from Transformers tokenization, Bayesian Neural Network, Hierarchical Neural Attention classifier, forward harmonic analysis.
How to cite this article: Bollikonda VB, Kiran KVD. Dark web crawling for automatic dark web classification using Bayesian Hierarchical Neural Attention Harmonic Network. Int J Drug Deliv Technol. 2026;16(3s): 960-972; DOI: 10.25258/ijddt.16.3s.116
Source of support: None.
Conflict of interest: None