THREAT/crawl: a Trainable, Highly-Reusable, and Extensible Automated Method and Tool to Crawl Criminal Underground Forums
| Autor: | Campobasso, Michele, Allodi, Luca |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Collecting data on underground criminal communities is highly valuable both for security research and security operations. Unfortunately these communities live within a constellation of diverse online forums that are difficult to infiltrate, may adopt crawling monitoring countermeasures, and require the development of ad-hoc scrapers for each different community, making the endeavour increasingly technically challenging, and potentially expensive. To address this problem we propose THREAT/crawl, a method and prototype tool for a highly reusable crawler that can learn a wide range of (arbitrary) forum structures, can remain under-the-radar during the crawling activity and can be extended and configured at the user will. We showcase THREAT/crawl capabilities and provide prime evaluation of our prototype against a range of active, live, underground communities. Comment: To be published in the Proceedings of the 17th Symposium on Electronic Crime Research (APWG eCrime 2022). Source code of the implemented solution available at https://gitlab.tue.nl/threat-crawl/THREATcrawl/ |
| Databáze: | arXiv |
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