2026-04-18T13:06:38Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72662023-01-19T01:05:23Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Sánchez, Raúl author Herrero Cosío, Álvaro author Corchado, Emilio author 2017-02 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 1367-0751 http://hdl.handle.net/10259/7266 10.1093/jigpal/jzw047 1368-9894 Network intrusion detection Network flow Neural projection Clustering MOVICAB-IDS Clustering extension of MOVICAB-IDS to distinguish intrusions in flow-based data