Skip to main content

Security Risks, Fake Degrees, and Other Fraud: A Topic Modelling Approach

  • Chapter
  • First Online:
Fake Degrees and Fraudulent Credentials in Higher Education

Part of the book series: Ethics and Integrity in Educational Contexts ((EIEC,volume 5))

Abstract

Topic modeling is an unsupervised machine learning technique commonly used in computer science as a research method. Machine learning utilizes data to make decisions and draw inferences through an algorithm. Topic modeling has yet to be used widely in education and, to our knowledge, not within the scope of security. This technique provided the main focus of this study. We investigate this gap using the problem of fake degrees and credentials, which also has limited scholarly review outside of technical solutions. The purchase and use of counterfeit degrees devalue the objective of higher education. If such a case is made public, an institution could be under fire and incur considerable financial loss due to reputational damage, legal fees, etc. Websites selling degrees are easily found with a quick search, and looking beneath the layers was essential to understanding this business. Data from 30 websites selling fake degrees were manually scraped, observations noted, and a topic model was built to identify risks within the dataset. Co-occurrences of keywords were visualized to provide a greater understanding of the data. As a result of our findings and further questions, web tools were applied to the IP addresses of the websites (e.g., geolocation and dynamic versus static connection) to provide supplemental data. We demonstrated that topic modeling can identify security risks by providing an environmental scan of the threat. This scan allowed us to isolate 23 risks. This analysis is beneficial for security professionals and senior leaders in higher education to ensure the solutions employed address the threat. Furthermore, this study also delivers value to researchers using text mining and those examining academic integrity or security. At the conclusion of this paper, 20 recommendations are outlined.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

Download references

Acknowledgements

We would like to thank Dr. Michael Weiss and Steve Fraser for their expertise.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jamie J. Carmichael .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Carmichael, J.J., Eaton, S.E. (2023). Security Risks, Fake Degrees, and Other Fraud: A Topic Modelling Approach. In: Eaton, S.E., Carmichael, J.J., Pethrick, H. (eds) Fake Degrees and Fraudulent Credentials in Higher Education . Ethics and Integrity in Educational Contexts, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-031-21796-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21796-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21795-1

  • Online ISBN: 978-3-031-21796-8

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics