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Integrating real-time analysis with the dendritic cell algorithm through segmentation

Gu, Feng; Greensmith, Julie; Aickelin, Uwe

Integrating real-time analysis with the dendritic cell algorithm through segmentation Thumbnail


Authors

Feng Gu

Uwe Aickelin



Abstract

As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantly better results in some cases, when compared to the standard DCA without segmentation. Therefore, we conclude that the segmentation is applicable to the DCA for the purpose of real-time analysis.

Citation

Gu, F., Greensmith, J., & Aickelin, U. (in press). Integrating real-time analysis with the dendritic cell algorithm through segmentation.

Conference Name GECCO '09: Proceedings of the 11th Genetic and Evolutionary Computation Conference
End Date Jul 12, 2009
Acceptance Date Jan 1, 2009
Deposit Date Jun 17, 2016
Peer Reviewed Peer Reviewed
Keywords Dendritic Cell Algorithm, Intrusion Detection Systems, Real-Time Analysis, Segmentation
Public URL https://nottingham-repository.worktribe.com/output/705189
Publisher URL http://dl.acm.org/citation.cfm?id=1570063&CFID=802004103&CFTOKEN=27246016
Related Public URLs http://dl.acm.org/citation.cfm?id=1569901
http://ima.ac.uk/papers/gu2009a.pdf
Additional Information doi:10.1145/1569901.1570063
Contract Date Jun 17, 2016

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