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,...
| Authors: | ; ; ; ; |
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| Format: | Dataset |
| Publication date: | 2023 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/148254 |
| Online Access: | https://hdl.handle.net/11441/148254 https://doi.org/10.12795/11441/148254 |
| Access Level: | Open access |
| Keyword: | 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 |
| _version_ | 1878741739923046400 |
|---|---|
| author | Díaz Verdejo, Jesús |
| author2 | Estepa Alonso, Rafael María Estepa Alonso, Antonio José Muñoz Calle, Francisco Javier Madinabeitia Luque, Germán |
| author2_role | author author author author |
| author_browse | Díaz Verdejo, Jesús Estepa Alonso, Antonio José Estepa Alonso, Rafael María Madinabeitia Luque, Germán Muñoz Calle, Francisco Javier |
| author_facet | Díaz Verdejo, Jesús Estepa Alonso, Rafael María Estepa Alonso, Antonio José Muñoz Calle, Francisco Javier Madinabeitia Luque, Germán |
| author_role | author |
| collection | idUS. Depósito de Investigación de la Universidad de Sevilla |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | dataset |
| id | ES_ea8a6f8d30c9a8db59417e7bdded32a7 |
| institution | Universidad de Sevilla (US) |
| instname_str | Universidad de Sevilla (US) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:idus.us.es:11441/148254 |
| publishDate | 2023 |
| publishDateSort | 2023 |
| reponame_str | idUS. Depósito de Investigación de la Universidad de Sevilla |
| spelling | Labeled HTTP requests dataset: Dataset Biblio-US17 Díaz Verdejo, Jesús Estepa Alonso, Rafael María Estepa Alonso, Antonio José Muñoz Calle, Francisco Javier Madinabeitia Luque, Germán 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 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. The dataset is organized in a tree structure (subdirectories) each containing different types of files or sets. As provided, 5 sets of files and two partitioning schemes are considered. The partition files are not directly provided but can be generated from the files using the provided script. The following sets of files (subdirs) are included: - RAW files: Initial registers (obtained after preprocessing and anonymization of real captured files). - LABEL files: Labels assigned during analysis. - CLEAN files: Registers considered as clean after sanitization. This is the full dataset to be used as normal traffic. - SID files: Information about SIDs triggered by used SIDS tools. - ATTACK files: Registers classified as attack (only LVL1 -indubituous- attacks). Registers in each set are organized in daily bins (files) named as biblio-2017-<mm>-<dd>.<ext>, being <mm> the number of the month, <dd> the day and <ext> an extension related to the type of content: - .raw for RAW files - .lbl for LBL files - .cl for CLEAN files - .sid for SID files - .att for ATTACK files https://hdl.handle.net/11441/148254 https://doi.org/10.12795/11441/148254 |
| spellingShingle | Labeled HTTP requests dataset: Dataset Biblio-US17 Díaz Verdejo, Jesús 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 |
| title | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| title_full | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| title_fullStr | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| title_full_unstemmed | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| title_short | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| title_sort | Labeled HTTP requests dataset: Dataset Biblio-US17 |
| topic | 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 |
| topic_facet | 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 |
| url | https://hdl.handle.net/11441/148254 https://doi.org/10.12795/11441/148254 |