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--- artefacts [2023/06/13 14:07] – mcampobasso
+++ artefacts [2023/10/13 12:31] (current) – mcampobasso
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 ^ Data      ^ Description       ^ Reference paper         ^ Access type ^ Link ^
-| INDICTED_CYBERCRIMINALS | List of indicted, convicted and arrested cybercriminals spanning from 2011 to 2021, extracted from https://arresttracker.com/ (currently offline). For each cybercriminal, the dataset reports name, surname, alias, date of indictment, conviction or arrest, short description of the accusations, and link to the source. Sources include the USA Department of Justice, Europol press releases, and media outlets.  | [[https://arxiv.org/pdf/2306.05898.pdf | M. Campobasso, L. Allodi (22nd Workshop on the Economics of Information Security (WEIS))]] | Licensed | [[https://dmp.win.tue.nl/|Data sharing link]]. For support, contact g[dot]gankhuyag[at]tue[dot]nl |
+| INDICTED_CYBERCRIMINALS | List of indicted, convicted and arrested cybercriminals spanning from 2011 to 2021, extracted from https://arresttracker.com/ (currently offline). For each cybercriminal, the dataset reports name, surname, alias, date of indictment, conviction or arrest, short description of the accusations, and link to the source. Sources include the USA Department of Justice, Europol press releases, and media outlets.  | [[https://arxiv.org/pdf/2306.05898.pdf | M. Campobasso, L. Allodi (22nd Workshop on the Economics of Information Security (WEIS))]] | Ad hoc collaborations | [[https://dmp.win.tue.nl/|Data sharing link]]. For support, contact g[dot]gankhuyag[at]tue[dot]nl |
-| IMPAAS_ECONOMY_DATASETS | Information about the advertised products, namely user profiles, crawled from the listing of an underground market providing Impersonation-as-a-Service products. Two datasets are available: (1) contains detailed information about the 25% random sample of daily advertised profiles on the market, collected on a daily basis; (2) is a collection of short information regarding the profiles scraped during each daily capture contained in (1), collected over the following 6 days to infer which profiles have been sold over time. In addition, we provide the scripts used to perform the pre-processing of the data. Finally, we provide the crawling infrastructure adopted to scrape the data from the market. | [[https://arxiv.org/abs/2303.03249|M. Campobasso, L. Allodi (32nd USENIX Security Symposium 2023) - Preprint version]] | Licensed | [[https://dmp.win.tue.nl/|Data sharing link]]. For support, contact g[dot]gankhuyag[at]tue[dot]nl  |
+| IMPAAS_ECONOMY_DATASETS | Information about the advertised products, namely user profiles, crawled from the listing of an underground market providing Impersonation-as-a-Service products. Two datasets are available: (1) contains detailed information about the 25% random sample of daily advertised profiles on the market, collected on a daily basis; (2) is a collection of short information regarding the profiles scraped during each daily capture contained in (1), collected over the following 6 days to infer which profiles have been sold over time. In addition, we provide the scripts used to perform the pre-processing of the data. Finally, we provide the crawling infrastructure adopted to scrape the data from the market. | [[https://arxiv.org/abs/2303.03249|M. Campobasso, L. Allodi (32nd USENIX Security Symposium 2023) - Preprint version]] | Ad hoc collaborations | [[https://dmp.win.tue.nl/|Data sharing link]]. For support, contact g[dot]gankhuyag[at]tue[dot]nl  |
-| IMPAAS_DATASETS| Information about the advertised products, namely user profiles, crawled from the listing of an underground market providing Impersonation-as-a-Service products. Two datasets are available; the former contains general information about all products available at scraping time, the latter details a random subset of profiles with information such as which credentials have been stolen from. In addition, a file R with the statistical analysis used for the paper can be available. | [[https://arxiv.org/abs/2009.04344|M. Campobasso, L. Allodi (ACM CCS 2020) - Preprint version]] | Licensed | {{ datasharing:tue_win_sec_data_sharing_impaas.doc | Download license}}|
+| IMPAAS_DATASETS| Information about the advertised products, namely user profiles, crawled from the listing of an underground market providing Impersonation-as-a-Service products. Two datasets are available; the former contains general information about all products available at scraping time, the latter details a random subset of profiles with information such as which credentials have been stolen from. In addition, a file R with the statistical analysis used for the paper can be available. | [[https://arxiv.org/abs/2009.04344|M. Campobasso, L. Allodi (ACM CCS 2020) - Preprint version]] | Ad hoc collaborations | {{ datasharing:tue_win_sec_data_sharing_impaas.doc | Download license}}|
 | MALMARKET_VULNS | CVEs published in a prominent Russian cybercrime market (as of April 2017). Reports CVE identifiers, prices, vendors, vulnerability characteristics, type of packaging, date of publication, date of disclosure, among other fields. *Note:* the SYM dataset is released [[https://securitylab.disi.unitn.it/doku.php?id=datasets|here]]. | [[https://arxiv.org/abs/1708.04866|L. Allodi (ACM CCS 2017)]]; [[http://www.win.tue.nl/~lallodi/allodi-tissec-14.pdf|L. Allodi, F. Massacci  (ACM TISSEC 2014)]]| Licensed | {{ :datasharing:tue_win_sec_data_sharing.doc | Download license}} |
 | MALMARKET | Unstructured dataset with the dumps of the market forum interactions (in Russian) spawning from the CVE trading reported in MALMARKET_VULNS.| [[https://arxiv.org/abs/1708.04866|L. Allodi (ACM CCS 2017)]]| Ad hoc collaborations | Send a short (max 1 page) research proposal to [[http://www.win.tue.nl/~lallodi|Dr. L. Allodi]]|