Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information
Cascavilla,Giuseppe Catolino,Gemma Sangiovanni,Mirella
Cascavilla,Giuseppe
Catolino,Gemma
Sangiovanni,Mirella
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.
Description
Publisher Copyright: © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Date
2022
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Science and Technology Publications, Lda
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Keywords
AI, Bert, DarkWeb, LSTM, Machine Learning, Natural-language Processing, RoBERTA, ULMFit
Citation
Cascavilla, G, Catolino, G & Sangiovanni, M 2022, Illicit Darkweb Classification via Natural-language Processing : Classifying Illicit Content of Webpages based on Textual Information. in S De Capitani di Vimercati & P Samarati (eds), SECRYPT 2022 - Proceedings of the 19th International Conference on Security and Cryptography. Proceedings of the International Conference on Security and Cryptography, vol. 1, Science and Technology Publications, Lda, pp. 620-626, 19th International Conference on Security and Cryptography, SECRYPT 2022, Lisbon, Portugal, 11/07/22. https://doi.org/10.5220/0011298600003283