Giuseppe Cascavilla, Gemma Catolino, Mirella Sangiovanni
{"title":"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","authors":"Giuseppe Cascavilla, Gemma Catolino, Mirella Sangiovanni","doi":"10.5220/0011298600003283","DOIUrl":null,"url":null,"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.","PeriodicalId":74779,"journal":{"name":"SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography","volume":"8 1","pages":"620-626"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information\",\"authors\":\"Giuseppe Cascavilla, Gemma Catolino, Mirella Sangiovanni\",\"doi\":\"10.5220/0011298600003283\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":74779,\"journal\":{\"name\":\"SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography\",\"volume\":\"8 1\",\"pages\":\"620-626\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0011298600003283\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0011298600003283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.