2026-04-28T11:34:17Zhttps://riubu.ubu.es/oai/request

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

Sánchez, Raúl Herrero Cosío, Álvaro Corchado, Emilio 2015-09-30T11:25:25Z 2015-09-30T11:25:25Z 2013-10 0196-9722 http://hdl.handle.net/10259/3859 10.1080/01969722.2013.803903 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 Spanish Ministry of Economy and Competitiveness with ref: TIN2010-21272-C02-01 (funded by the European Regional Development Fund) and SA405A12-2 from Junta de Castilla y Leon. application/pdf eng Taylor & Francis Cybernetics and Systems: An International Journal. 2013, V. 44, n. 6-7, p. 505-532 http://dx.doi.org/10.1080/01969722.2013.803903 info:eu-repo/grantAgreement/MINECO/TIN2010-21272-C02-01 info:eu-repo/grantAgreement/JCyL/SA405A12-2 Automatic response Clustering Computational intelligence Exploratory projection pursuit k-means Network intrusion detection Informática Computer science Visualization and clustering for SNMP intrusion detection info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess