Using an evolutionary neural network for web intrusion detection

dc.contributor.author	Ξύδας, Ιωάννης	el
dc.contributor.author	Μιαούλης, Γεώργιος	el
dc.contributor.author	Bonnefoi, Pierre-François	fr
dc.contributor.author	Πλεμμένος, Δημήτρης	el
dc.contributor.author	Ghazanfarpour, Djamchid	fr
dc.date.accessioned	2015-05-08T08:28:13Z
dc.date.available	2015-05-08T08:28:13Z
dc.date.issued	2015-05-08
dc.identifier.uri	http://hdl.handle.net/11400/9921
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.iasted.org/conferences/	en
dc.source	http://users.teiath.gr/yxydas/Paper3_xydas.pdf	el
dc.subject	Genetic algorithms
dc.subject	EANN
dc.subject	Web attacks
dc.subject	Visual analytics
dc.subject	Γενετικοί αλγόριθμοι
dc.subject	Εφαρμογές Μηχανικής Νευρωνικών Δικτύων
dc.subject	Διαδικτυακές επιθέσεις
dc.subject	Οπτική αναλυτική
dc.title	Using an evolutionary neural network for web intrusion detection	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Computer science
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2007004134
heal.identifier.secondary	ISBN: 978-0-88086-709-3
heal.language	en
heal.access	free
heal.recordProvider	Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2008-02
heal.bibliographicCitation	Xydas, I., Miaoulis, G, Bonnefoi, P.F., Plemenos, D. and Ghazanfarpour, D. (2008) Using an evolutionary neural network for web intrusion detection. In "Artificial Intelligence and Applications 2008". Innsbruck: IASTED. Available from: http://users.teiath.gr/yxydas/Paper3_xydas.pdf [Accessed: 08/05/2015].	en
heal.abstract	Due to the complicated nature of detecting actual intrusions, most current Network Intrusion Detection Systems (NIDS) place the burden of distinguishing an actual attack from a large set of false alarms on the security analyst, resulting in a significant cognitive load. Artificial Intelligence combined with Visualization will take advantage of human perceptual abilities and expertise to amplify cognition. In this paper we will describe an Evolutionary Artificial Neural Network (EANN) used as the knowledge base for the classification of web attacks in a prototype system. The aforementioned system is a surveillance aid for the security analyst, offering him a user friendly visual tool to detect anomalies in web requests by exploring 3D graphs, to understand quickly the kind of undergoing attack by means of colours and afford him the possibility to navigate into the payload of the web request for further analysis and adequate response. The EANN system is an improvement of our original work that used a supervised multilayer Artificial Neural Network (ANN) as the web attacks classifier.	en
heal.publisher	IASTED	en
heal.fullTextAvailability	true
heal.conferenceName	Artificial Intelligence and Applications 2008	en
heal.conferenceItemType	full paper