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Crime VIP: A Closed-Access Underground Hacking Forum
| dc.contributor.author | Mischinger, Mariella | |
| dc.contributor.author | Pastrana, Sergio | |
| dc.contributor.author | Suarez-Tangil, Guillermo | |
| dc.date.accessioned | 2026-07-17T09:17:28Z | |
| dc.date.available | 2026-07-17T09:17:28Z | |
| dc.date.issued | 2026-05 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12761/2058 | |
| dc.description.abstract | We present XIN, the dataset of a closed-access Russian-English underground hacking forum. It contains a collection of ≈1.3M posts from over 20 years (Feb 2005-Aug 2025), and --- to the best of our knowledge --- is the largest collection of a closed-access underground hacking forum available for research. While there is a wide range of underground forum datasets available, there is a lack of non-English forums, and especially closed-access forums. Those are particularly challenging to crawl as the access is gated. Hence, a limited few-shot opportunity requires extreme care when crawling to avoid detection, which leads to account banning. Our stealthy data collection spanned 5 years (2020-2025). The statistical analysis of our data mirrors how cybercrime gradually shifted from technical, hands-on hacking to an industry where different building blocks can be assembled and applied in the absence of a broad or profound technical understanding. This dataset will contribute to a more complete evaluation of the cybercriminal landscape, shedding light on the activity that happens in closed-door, non-English communities. | es |
| dc.language.iso | eng | es |
| dc.title | Crime VIP: A Closed-Access Underground Hacking Forum | es |
| dc.type | conference object | es |
| dc.conference.date | 27-29 May 2026 | es |
| dc.conference.place | Los Angeles, USA | es |
| dc.conference.title | International Conference on Web and Social Media | * |
| dc.event.type | conference | es |
| dc.pres.type | paper | es |
| dc.rights.accessRights | open access | es |
| dc.acronym | ICWSM | * |
| dc.rank | A | * |
| dc.description.awards | Best Dataset Paper Honorable Mention | es |
| dc.description.refereed | TRUE | es |
| dc.description.status | pub | es |


