Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information
Giuseppe Cascavilla,
Gemma Catolino,
Mirella Sangiovanni
Abstract:This work aims at expanding previous works done in the context of illegal activities classification, performing three different steps. First, we created a heterogeneous dataset of 113995 onion sites and dark marketplaces. Then, we compared pre-trained transferable models, i.e., ULMFit (Universal Language Model Fine-tuning), Bert (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly optimized BERT approach) with a traditional text classification approach like LSTM (Long short-term mem… Show more
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