Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Abstract This paper introduces Biblio-US17, a labeled dataset collected over 6 months from the log files of a popular public website at the University of Seville. It contains 47 million records, each including the method, uniform resource identifier (URI) and associated response code and size of eve...

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Bibliographic Details
Main Authors: Jesús Díaz-Verdejo, Rafael Estepa, Antonio Estepa, Javier Muñoz-Calle, Germán Madinabeitia
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Cybersecurity
Subjects:
Anomaly detection
Intrusion detection systems
Data acquisition
Training datasets
Web application filters
Biblio-US17 dataset
Online Access:https://doi.org/10.1186/s42400-024-00336-3
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https://doi.org/10.1186/s42400-024-00336-3

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