No full text
Paper published in a journal (Scientific congresses, symposiums and conference proceedings)
An Approach to Intrusion Detection by Means of Idiotypic Networks Paradigm
Ostaszewski, Marek; Bouvry, Pascal; Seredynski, Franciszek
2008In IEEE World Congress on Computational Intelligence, WCCI 2008, Congress on Evolutionary Computation CEC 2008, Honk-Kong, June
Peer reviewed
 

Files


Full Text
No document available.

Send to



Details



Keywords :
intrusion detection; artificial immune systems; metaheuristics
Abstract :
[en] In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MITpsila99 dataset and outperforms previous approaches for DoS detection applied to this set.
Disciplines :
Computer science
Identifiers :
UNILU:UL-CONFERENCE-2009-294
Author, co-author :
Ostaszewski, Marek  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Seredynski, Franciszek
External co-authors :
yes
Language :
English
Title :
An Approach to Intrusion Detection by Means of Idiotypic Networks Paradigm
Publication date :
2008
Event name :
IEEE World Congress on Computational Intelligence, WCCI 2008, Congress on Evolutionary Computation CEC 2008, Honk-Kong, June
Event date :
June 2008
By request :
Yes
Audience :
International
Journal title :
IEEE World Congress on Computational Intelligence, WCCI 2008, Congress on Evolutionary Computation CEC 2008, Honk-Kong, June
Publisher :
IEEE Computer Society
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 20 January 2016

Statistics


Number of views
89 (0 by Unilu)
Number of downloads
0 (0 by Unilu)

Scopus citations®
 
0
Scopus citations®
without self-citations
0
WoS citations
 
0

Bibliography


Similar publications



Contact ORBilu