| Reference : Coevolutionary-based Mechanisms for Network Anomaly Detection |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/3308 | |||
| Coevolutionary-based Mechanisms for Network Anomaly Detection | |
| English | |
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) >] | |
| 2007 | |
| Journal of Mathematical Modelling and Algorithms | |
| Springer | |
| 6 | |
| 3 | |
| 411-431 | |
| Yes | |
| International | |
| 1570-1166 | |
| 1572-9214 | |
| Berlin | |
| Germany | |
| [en] Intrusion detection ; Artificial Immune Systems ; Coevolutionary algorithms | |
| [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. | |
| Researchers | |
| http://hdl.handle.net/10993/3308 |
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