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| dc.contributor.author | Herrero Cosío, Álvaro | |
| dc.contributor.author | Corchado Rodríguez, Emilio Santiago | |
| dc.contributor.author | Sáiz, José M. | |
| dc.date.accessioned | 2017-09-06T09:16:26Z | |
| dc.date.available | 2017-09-06T09:16:26Z | |
| dc.date.issued | 2005-07 | |
| dc.identifier.citation | Intelligent Data Engineering and Automated Learning - IDEAL 2005 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 3578, pp. 187-194. | |
| dc.identifier.isbn | 978-3-540-26972-4 (Print) / 978-3-540-31693-0 (Online) | |
| dc.identifier.issn | 0302-9743 (Print) / 1611-3349 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/10366/135088 | |
| dc.description.abstract | Thework presented in this paper shows the capability of a connectionist model, based on a statistical technique called Exploratory Projection Pursuit (EPP), to identify anomalous situations related to the traffic which travels along a computer network. The main novelty of this research resides on the fact that the connectionist architecture used here has never been applied to the field of IDS (Intrusion Detection Systems) and network security. The IDS presented is used as a method to investigate the traffic which travels along the analysed network, detecting SNMP (Simple Network Management Protocol) anomalous traffic patterns. In this paper we have focused our attention on the study of two interesting and dangerous anomalous situations: a port sweep and a MIB (Management Information Base) information transfer. The presented IDS is a useful visualization tool for network administrators to study anomalous situations related to SNMP and decide if they are intrusions or not. To show the power of the method, we illustrate our research by using real intrusion detection scenario specific data sets. | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.publisher | Springer Science + Business Media | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
| dc.subject | Computer Science | |
| dc.title | Identification of Anomalous SNMP Situations Using a Cooperative Connectionist Exploratory Projection Pursuit Model | |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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