基于自然语言处理的非法暗网分类:基于文本信息的网页非法内容分类

Giuseppe Cascavilla, Gemma Catolino, Mirella Sangiovanni
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引用次数: 1

摘要

:本工作旨在扩展以往在非法活动分类背景下所做的工作,执行三个不同的步骤。首先,我们创建了一个包含113995个洋葱网站和黑市的异构数据集。然后,我们比较了预训练的可转移模型,即ULMFit(通用语言模型微调),Bert(来自变形变压器的双向编码器表示)和RoBERTa(鲁棒优化的Bert方法)与传统的文本分类方法,如LSTM(长短期记忆)神经网络。最后,我们开发了两种非法活动分类方法,一种用于暗网上的非法内容,另一种用于识别特定类型的药物。结果表明,Bert获得了最好的方法,对暗网的一般内容和药物类型进行分类,准确率分别为96.08%和91.98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information
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.
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