Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/9921
Title: Using an evolutionary neural network for web intrusion detection
Authors: Ξύδας, Ιωάννης
Μιαούλης, Γεώργιος
Πλεμμένος, Δημήτρης
Bonnefoi, Pierre-François
Ghazanfarpour, Djamchid
Item type: Conference publication
Conference Item Type: Full Paper
Keywords: Genetic algorithms;EANN;Web attacks;Visual analytics;Γενετικοί αλγόριθμοι;Εφαρμογές Μηχανικής Νευρωνικών Δικτύων;Διαδικτυακές επιθέσεις;Οπτική αναλυτική
Subjects: Technology
Computer science
Τεχνολογία
Πληροφορική
Issue Date: 8-May-2015
Feb-2008
Date of availability: 8-May-2015
Publisher: IASTED
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.
Language: English
Citation: 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].
Conference: Artificial Intelligence and Applications 2008
Access scheme: Publicly accessible
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/9921
Appears in Collections:Δημοσιεύσεις

Files in This Item:
File Description SizeFormat 
Using an evolutionary neural network for web intrusion detection.pdf1.45 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons