Authors:
Giuseppe Cascavilla
1
;
Gemma Catolino
2
and
Mirella Sangiovanni
2
Affiliations:
1
Eindhoven University of Technology, Jheronimus Academy of Data Science, The Netherlands
;
2
Tilburg University, Jheronimus Academy of Data Science, The Netherlands
Keyword(s):
Natural-language Processing, DarkWeb, Bert, RoBERTA, Machine Learning, ULMFit, LSTM, AI.
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 memory) neural networks. Finally, we developed two illegal activities classification approaches, one for illicit content on the Dark Web and one for identifying the specific types of drugs. Results show that Bert obtained the best approach, classifying the dark web’s general content and the types of Drugs with 96.08% and 91.98% of accuracy.