Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/72373
Title: Self-organizing maps for early detection of denial of service attacks
Authors: Pérez Del Pino, Miguel Angel 
García Báez, Patricio 
Fernández López, Pablo Carmelo 
Suarez Araujo, C. P. 
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2012
Journal: Studies in Computational Intelligence 
Conference: 14th IEEE International Conference on Intelligent Engineering Systems 
Abstract: Detection and early alert of Denial of Service (DoS) attacks are very important actions to make appropriate decisions in order to minimize their negative impact. DoS attacks have been catalogued as of high-catastrophic index and hard to defend against. Our study presents advances in the area of computer security against DoS attacks. In this chapter, a flexible method is presented, capable of effectively tackling and overcoming the challenge of DoS (and distributed DoS) attacks using a CISDAD (Computer Intelligent System for DoS Attacks Detection). It is a hybrid intelligent system with a modular structure: a pre-processing module (non neural) and a processing module based on Kohonen Self-Organizing artificial neural networks. The proposed system introduces an automatic differential detection of several Normal Traffic and several Toxic Traffics, clustering them upon its Transport-Layer-Protocol behavior. Two computational studies of CISDAD working with real networking traffic will be described, showing a high level of effectiveness in the CISDAD detection process. Finally, in this chapter, the possibility for specific adaptation to the Healthcare environment that CISDAD can offer is introduced.
URI: http://hdl.handle.net/10553/72373
ISBN: 978-3-642-23228-2
ISSN: 1860-949X
DOI: 10.1007/978-3-642-23229-9_9
Source: Recent Advances In Intelligent Engineering Systems [ISSN 1860-949X], v. 378, p. 195-219, (2012)
Appears in Collections:Actas de congresos
Show full item record

SCOPUSTM   
Citations

1
checked on Sep 26, 2021

WEB OF SCIENCETM
Citations

2
checked on Sep 26, 2021

Page view(s)

34
checked on Sep 25, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.