2026-04-28T11:35:20Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/38592022-12-20T11:20:01Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Sánchez, Raúl author Herrero Cosío, Álvaro author Corchado, Emilio author 2013-10 Accurate intrusion detection is still an open challenge. The present work aims at being one step toward that purpose by studying the combination of clustering and visualization techniques. To do that, the mobile visualization connectionist agent-based intrusion detection system (MOVICAB-IDS), previously proposed as a hybrid intelligent IDS based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the simple network management network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in the present article. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection 0196-9722 http://hdl.handle.net/10259/3859 10.1080/01969722.2013.803903 Automatic response Clustering Computational intelligence Exploratory projection pursuit k-means Network intrusion detection Visualization and clustering for SNMP intrusion detection