Labeled HTTP requests dataset: Dataset Biblio-US17
This dataset contains a set of anonymized and labeled HTTP requests (selected fields) from the logs of a real-in-production web server at the library of the University of Seville during 6.5 months in 2017. The dataset has been sanitized using a supervised methodology as proposed in: - Díaz-Verdejo,...
| Autores: | , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/148254 |
| Acceso en línea: | https://hdl.handle.net/11441/148254 https://doi.org/10.12795/11441/148254 |
| Access Level: | acceso abierto |
| Palabra clave: | Anomaly based intrusion detection data acquisition training datasets web application filters Detección de intrusos basada en anomalías adquisición de datos conjuntos de datos de entrenamiento filtros de aplicaciones web |
| Sumario: | This dataset contains a set of anonymized and labeled HTTP requests (selected fields) from the logs of a real-in-production web server at the library of the University of Seville during 6.5 months in 2017. The dataset has been sanitized using a supervised methodology as proposed in: - Díaz-Verdejo, Jesús E.; Estepa, Antonio; Estepa, Rafael; Madinabeitia, German; Muñoz-Calle, Javier, "A methodology for conducting efficient sanitization of HTTP training datasets", Future Generation Computer Systems, vol. 109, pp. 67–82, 2020. https://doi.org/10.1016/j.future.2020.03.033. |
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