Show simple item record

dc.contributor.authorHerrero Cosío, Álvaro
dc.contributor.authorCorchado Rodríguez, Emilio Santiago
dc.identifier.citationHybrid Artificial Intelligence Systems Lecture Notes in Computer Science. Third International Workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008. Proceedings. Lecture Notes in Computer Science. Volumen 5271, pp. 247-256.
dc.identifier.isbn978-3-540-87655-7 (Print) / 978-3-540-87656-4 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.description.abstractAn increasing effort has being devoted to researching on the field of Intrusion Detection Systems (IDS’s). A wide variety of artificial intelligence techniques and paradigms have been applied to this challenging task in order to identify anomalous situations taking place within a computer network. Among these techniques is the neural network approach whose models (or most of them) have some difficulties in processing traffic data “on the fly”. The present work addresses this weakness, emphasizing the importance of an appropriate segmentation of raw traffic data for a successful network intrusion detection relying on unsupervised neural models. In this paper, the presented neural model is embedded in a hybrid artificial intelligence IDS which integrates the case based reasoning and multiagent paradigms.
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titleTraffic Data Preparation for a Hybrid Network IDS

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported