{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/identifying-products-in-online-cybercrime","title":"Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation","arxiv_id":"1708.09609","date":"2017-08-31","proceeding":"EMNLP 2017 9","authors":["Greg Durrett","Jonathan K. Kummerfeld","Taylor Berg-Kirkpatrick","Rebecca S. Portnoff","Sadia Afroz","Damon McCoy","Kirill Levchenko","Vern Paxson"],"abstract":"One weakness of machine-learned NLP models is that they typically perform\npoorly on out-of-domain data. In this work, we study the task of identifying\nproducts being bought and sold in online cybercrime forums, which exhibits\nparticularly challenging cross-domain effects. We formulate a task that\nrepresents a hybrid of slot-filling information extraction and named entity\nrecognition and annotate data from four different forums. Each of these forums\nconstitutes its own \"fine-grained domain\" in that the forums cover different\nmarket sectors with different properties, even though all forums are in the\nbroad domain of cybercrime. We characterize these domain differences in the\ncontext of a learning-based system: supervised models see decreased accuracy\nwhen applied to new forums, and standard techniques for semi-supervised\nlearning and domain adaptation have limited effectiveness on this data, which\nsuggests the need to improve these techniques. We release a dataset of 1,938\nannotated posts from across the four forums.","url_abs":"http://arxiv.org/abs/1708.09609v1","url_pdf":"http://arxiv.org/pdf/1708.09609v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"identifying-products-in-online-cybercrime","repo_url":"https://github.com/ccied/ugforum-analysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"},{"task_slug":"slot-filling-1","task_name":"slot-filling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}