reethikar / GovHTTPS-Data

The dataset for the IMC'20 paper titled "Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption"

HTTPS Adoption Measurement in Governments Worldwide

This is the public dataset published as auxilary material for the paper accepted to Internet Measurement Conference (IMC'20) titled "Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption"

The dataset is present in dataset.csv and includes the whitelist of the websites which were obtained as a part of the whitelist process both manually and crowdsourcing using Mechanical Turk. The countries and their domain country codes are available in country-extensions.csv file. A breakdown of the same dataset split by country is available in the country/ directory.

Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption

By: Sudheesh Singanamalla, Esther Han Beol Jang, Richard Anderson, Tadayoshi Kohno, and Kurtis Heimerl

@inproceedings{10.1145/3419394.3423645,
    author = {Singanamalla, Sudheesh and Jang, Esther Han Beol and Anderson, Richard and Kohno, Tadayoshi and Heimerl, Kurtis},
    title = {Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption},
    year = {2020},
    isbn = {978-1-4503-8138-3/20/10},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3419394.3423645},
    doi = {10.1145/3419394.3423645},
    booktitle = {Proceedings of the Internet Measurement Conference},
    location = {Virtual Event},
    series = {IMC '20}
}

or

Sudheesh Singanamalla, Esther Han Beol Jang, Richard Anderson, Tadayoshi Kohno, and Kurtis Heimerl. 2020. 
Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption. In ACM Internet 
Measurement Conference (IMC ’20), October 27–29, 2020, Virtual Event, USA. ACM, New York, NY, USA, 
21 pages. https://doi.org/10.1145/3419394.3423645

Want to help us curate more government websites to the dataset? Please send a pull request.

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Note: The filters applied in the process of curation of this dataset could include entries which are very intentionally close to official government extensions eg. abcgov.us instead of abc.gov.us as indicated in the paper. We do our best to filter them (eg. trivago.ccTLD which matches go.ccTLD extensions) in our analysis in the paper but some errors could have crept in. We however leave these entries in this dataset as is and do not remove them for any future work on phishing detections.