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Web Scams Detection System

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Foundations and Practice of Security (FPS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14551))

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Abstract

Web-based scams rely on scam websites to provide fraudulent business or fake services to steal money and sensitive information from unsuspecting victims. Despite many researchers’ efforts to develop anti-scam detection techniques, their main focus has been on understanding, detecting, and analyzing scam sites. State-of-the-art anti-scam research still faces several challenges, such as acquiring a properly labeled scam dataset, especially when there is no blacklist, central repository, or previous large-scale analysis. The researchers have created labeled datasets in different ways, such as manually collecting and labeling the dataset or using a semi-automatic crawler followed by manual inspection. However, this process requires previous knowledge and understanding of the scam and much manual work.

In this paper, we propose a data-driven model to create a labeled training dataset for web-based scams that have a web presence. Given a small scam sample, our model formulates scam-related search queries and uses them on multiple search engines to search for, and collect, potential scam pages. After collecting a sufficiently large corpus of web pages, our model semi-automatically clusters the search results and creates a labeled training dataset with minimal human interaction. We have validated our model using two different scam types that we have studied in our previous work. We tested our classifiers against the databases of web pages we collected during our previous analysis of the scams and successfully detected more than 87% of the scam pages while maintaining a false positive value as low as 0.23%.

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Notes

  1. 1.

    https://www.scamwatch.gov.au/scam-statistics.

  2. 2.

    We describe applying the model to create and validate the training datasets for identifying BGS and GHS scams in a separate report hosted with our dataset https://bit.ly/DatasetPaper.

  3. 3.

    https://trends.google.com/trends/?geo=US.

  4. 4.

    https://urlscan.io/.

  5. 5.

    https://website.informer.com/.

  6. 6.

    https://www.cutestat.com/.

  7. 7.

    https://web.archive.org/.

  8. 8.

    https://www.alexa.com/.

  9. 9.

    http://chromedriver.chromium.org/.

  10. 10.

    https://selenium-python.readthedocs.io.

  11. 11.

    https://pypi.org/project/beautifulsoup4/.

  12. 12.

    We attached the dataset creation and validation process on a separate report hosted with our dataset https://bit.ly/DatasetPaperReport.

  13. 13.

    We collected the domains by crawling cutestat.com search engine and a blacklist maintained by Bitcoin.fr.

  14. 14.

    In our analysis, for both the manual and automated approaches, we did not include any automated process, such as crawling time. We only included the time we spent manually searching, inspecting, and labeling the pages.

  15. 15.

    https://websitesetup.org/news/internet-facts-stats/, accessed in 2022.

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Authors and Affiliations

  1. University of Ottawa, Ottawa, ON, Canada

    Emad Badawi, Guy-Vincent Jourdan & Iosif-Viorel Onut

  2. IBM Centre for Advanced Studies, Ottawa, ON, Canada

    Emad Badawi, Guy-Vincent Jourdan & Iosif-Viorel Onut

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  1. Emad Badawi
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Correspondence to Emad Badawi .

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Editors and Affiliations

  1. University of Bordeaux, Bordeaux, France

    Mohamed Mosbah

  2. Toulouse III - Paul Sabatier University, Toulouse, France

    Florence Sèdes

  3. Université Laval, Québec, QC, Canada

    Nadia Tawbi

  4. University of Bordeaux, Bordeaux, France

    Toufik Ahmed

  5. Polytechnique Montréal, Montreal, QC, Canada

    Nora Boulahia-Cuppens

  6. Telecom SudParis, Palaiseau, France

    Joaquin Garcia-Alfaro

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Badawi, E., Jourdan, GV., Onut, IV. (2024). Web Scams Detection System. In: Mosbah, M., Sèdes, F., Tawbi, N., Ahmed, T., Boulahia-Cuppens, N., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2023. Lecture Notes in Computer Science, vol 14551. Springer, Cham. https://doi.org/10.1007/978-3-031-57537-2_11

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