Between 2010 and 2013, we conducted numerous measurements of search-redirection attacks, and of the online pharmaceutical trade. In an effort to make our research reproducible, and to allow others to use the relatively rich datasets we collected for further analysis, we have decided to make publicly available the vast majority of our measurements.
All the data we collected was public at some point or another, and did not require any form privileged access to collect. The papers we wrote on the subject [1, 2, 3] detail our collection methodologies and numerous analyses of the datasets.
We do not make any claim that our data reflect anything else than observations made at the time of data collection. For instance, certain datasets contain URLs of sites that were compromised at a point in time. That certainly does not mean that these sites are still compromised, or even still exist.
We hope the research community will find this data useful. Please feel free to contact the PI, Nicolas Christin, if you have any questions; but please first carefully read the relevant papers and the description of the data given on the download pages.
The data is made available under a Creative Commons Attribution-NonCommercial 4.0 International License. If you use any of this dataset, please do not link to this page. Instead, please cite the relevant associated papers.
 Nektarios Leontiadis, Tyler Moore, and Nicolas Christin. Measuring and Analyzing Search-Redirection Attacks in the Illicit Online Prescription Drug Trade. In Proceedings of the 20th USENIX Security Symposium (USENIX Security'11), pages 281-298. San Francisco, CA. August 2011.
 Nektarios Leontiadis, Tyler Moore, and Nicolas Christin. Pick Your Poison: Pricing and Inventories at Unlicensed Online Pharmacies. In Proceedings of the 14th ACM Conference on Electronic Commerce (EC'13), pages 621-638. Philadelphia, PA. June 2013.
 Nektarios Leontiadis, Tyler Moore, and Nicolas Christin. A Nearly Four-Year Longitudinal Study of Search-Engine Poisoning. To appear in Proceedings of the 21st ACM Conference on Computer and Communication Security (CCS'14). Scottsdale, AZ. November 2014.
This research was partially supported by CyLab at Carnegie Mellon under grant DAAD19-02-1-0389 from the Army Research Office; by the National Science Foundation under ITR award CCF-0424422 (TRUST), and SaTC award CNS-1223762; and by the Department of Homeland Security Science and Technology Directorate, Cyber Security Division (DHS S&T/CSD), the Government of Australia and SPAWAR Systems Center Pacific (through BAA-11.02, contract number N66001-13-C-0131). This website represents the position of the authors and not that of the aforementioned agencies.