Leveraging ML to fight back the cybersecurity risk of credential stuffing attacks
Conti, Lorenzo (A.A. 2023/2024) Leveraging ML to fight back the cybersecurity risk of credential stuffing attacks. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 71. [Master's Degree Thesis]
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Abstract/Index
Theory/literature review. Credential stuffing. RBA (Risk-based authentication). Highly imbalanced data classification. Methods. Dataset description. Analyzing the trend of failed/total login attempts in detail. Comparison between most popular countries in terms of both total/failed login attempts. Inspecting the “round-trip time [ms]” feature in more detail. Geo-distribution of account takeover instances. Relationship between the target and other predictors. Is there any hidden information behind “user ID” that could have an impact on the target? Clustering the dataset after removing “is account takeover”. Data preprocessing. Model building. Performance metrics. Results and discussion. Model results on the entire dataset. Model results on the under-sampled dataset.
References
Bibliografia: pp. 69-71.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Martino, Alessio |
| Thesis Co-Supervisor: | Coppa, Emilio |
| Academic Year: | 2023/2024 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 17 Dec 2024 11:03 |
| Last Modified: | 17 Dec 2024 11:03 |
| URI: | https://tesi.luiss.it/id/eprint/40652 |
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