2026-04-18T13:06:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72662023-01-19T01:05:23Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Sánchez, Raúl
author
Herrero Cosío, Álvaro
author
Corchado, Emilio
2023-01-18T12:03:50Z
2023-01-18T12:03:50Z
2017-02
1367-0751
http://hdl.handle.net/10259/7266
10.1093/jigpal/jzw047
1368-9894
Much effort has been devoted to research on intrusion detection (ID) in recent years because intrusion strategies and technologies are constantly and quickly evolving. As an innovative solution based on visualization, MObile VIsualisation Connectionist Agent-Based IDS was previously proposed, conceived as a hybrid-intelligent ID System. It was designed to analyse
continuous network data at a packet level and is extended in present paper for the analysis of flow-based traffic data. By
incorporating clustering techniques to the original proposal, network flows are investigated trying to identify different types
of attacks. The analysed real-life data (the well-known dataset from the University of Twente) come from a honeypot directly
connected to the Internet (thus ensuring attack-exposure) and is analysed by means of clustering and neural techniques, individually and in conjunction. Promising results are obtained, proving the validity of the proposed extension for the analysis
of network flow data
eng
Network intrusion detection
Network flow
Neural projection
Clustering
MOVICAB-IDS
Clustering extension of MOVICAB-IDS to distinguish intrusions in flow-based data
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/7266/1/S%c3%a1nchez-igpl_2017.pdf
File
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application/pdf
Sánchez-igpl_2017.pdf