Article (Scientific journals)
Coevolutionary-based Mechanisms for Network Anomaly Detection
Ostaszewski, Marek; Seredynski, Franciszek; Bouvry, Pascal
2007In Journal of Mathematical Modelling and Algorithms, 6 (3), p. 411-431
Peer reviewed
 

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Keywords :
Intrusion detection; Artificial Immune Systems; Coevolutionary algorithms
Abstract :
[en] The paper presents an approach based on the principles of immune systems applied to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of the network behavior based on the self-nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. The structures corresponding to self-space are built using a training set from this space. The hyperrectangular detectors covering nonself space are created using a niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. The results of experiments show a high quality of intrusion detection, which outperform the quality of the recently proposed approach based on a hypersphere representation of the self-space.
Disciplines :
Computer science
Identifiers :
UNILU:UL-ARTICLE-2008-446
Author, co-author :
Ostaszewski, Marek  ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Seredynski, Franciszek;  Polish Academy of Sciences > Institute for Computer Sciences
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
Coevolutionary-based Mechanisms for Network Anomaly Detection
Publication date :
2007
Journal title :
Journal of Mathematical Modelling and Algorithms
ISSN :
1572-9214
Publisher :
Springer, Berlin, Germany
Volume :
6
Issue :
3
Pages :
411-431
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 03 July 2013

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