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,...

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Detalhes bibliográficos
Autores: Díaz Verdejo, Jesús, Estepa Alonso, Rafael María, Estepa Alonso, Antonio José, Muñoz Calle, Francisco Javier, Madinabeitia Luque, Germán
Formato: conjunto de datos
Fecha de publicación:2023
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/148254
Acesso em linha:https://hdl.handle.net/11441/148254
https://doi.org/10.12795/11441/148254
Access Level:acceso abierto
Palavra-chave: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
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spelling Labeled HTTP requests dataset: Dataset Biblio-US17Díaz Verdejo, JesúsEstepa Alonso, Rafael MaríaEstepa Alonso, Antonio JoséMuñoz Calle, Francisco JavierMadinabeitia Luque, GermánAnomaly based intrusion detectiondata acquisitiontraining datasetsweb application filtersDetección de intrusos basada en anomalíasadquisición de datosconjuntos de datos de entrenamientofiltros de aplicaciones webThis 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 filesIngeniería TelemáticaTIC154: Departamento de Ingeniería TelemáticaEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Ministerio de Ciencia e InnovaciónJunta de Andalucía (Consejería de Transformación Económica, Industria, Conocimiento y Universidades)Universidad de SevillaMuñoz Calle, Francisco JavierEstepa Alonso, Rafael MaríaDíaz Verdejo, JesúsMuñoz Calle, Francisco Javier2023info:eu-repo/semantics/datasetDatasettext/plainapplication/octet-streamtext/plainapplication/gziphttps://hdl.handle.net/11441/148254https://doi.org/10.12795/11441/148254reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésDíaz Verdejo, J., Estepa Alonso, R.M., Estepa Alonso, A.J., Muñoz Calle, F.J. y Madinabeitia Luque, G. (2025). Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17. Cybersecurity, 8, 38. https://doi.org/10.1186/s42400-024-00336-3.https://hdl.handle.net/11441/174115PI-1736/22/2017A-TIC-224-UGR20PID2020-115199RB-I00PYC20-RE-087-USEinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1482542026-06-17T12:51:07Z
dc.title.none.fl_str_mv Labeled HTTP requests dataset: Dataset Biblio-US17
title Labeled HTTP requests dataset: Dataset Biblio-US17
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_short 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_sort Labeled HTTP requests dataset: Dataset Biblio-US17
dc.creator.none.fl_str_mv Díaz Verdejo, Jesús
Estepa Alonso, Rafael María
Estepa Alonso, Antonio José
Muñoz Calle, Francisco Javier
Madinabeitia Luque, Germán
author Díaz Verdejo, Jesús
author_facet Díaz Verdejo, Jesús
Estepa Alonso, Rafael María
Estepa Alonso, Antonio José
Muñoz Calle, Francisco Javier
Madinabeitia Luque, Germán
author_role author
author2 Estepa Alonso, Rafael María
Estepa Alonso, Antonio José
Muñoz Calle, Francisco Javier
Madinabeitia Luque, Germán
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Telemática
TIC154: Departamento de Ingeniería Telemática
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Ministerio de Ciencia e Innovación
Junta de Andalucía (Consejería de Transformación Económica, Industria, Conocimiento y Universidades)
Universidad de Sevilla
Muñoz Calle, Francisco Javier
Estepa Alonso, Rafael María
Díaz Verdejo, Jesús
Muñoz Calle, Francisco Javier
dc.subject.none.fl_str_mv 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 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
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.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
Dataset
format dataset
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/148254
https://doi.org/10.12795/11441/148254
url https://hdl.handle.net/11441/148254
https://doi.org/10.12795/11441/148254
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Díaz Verdejo, J., Estepa Alonso, R.M., Estepa Alonso, A.J., Muñoz Calle, F.J. y Madinabeitia Luque, G. (2025). Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17. Cybersecurity, 8, 38. https://doi.org/10.1186/s42400-024-00336-3.
https://hdl.handle.net/11441/174115
PI-1736/22/2017
A-TIC-224-UGR20
PID2020-115199RB-I00
PYC20-RE-087-USE
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/plain
application/octet-stream
text/plain
application/gzip
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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