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

Gu, Feng; Greensmith, Julie; Aickelin, Uwe

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. Integrating real-time analysis with the dendritic cell algorithm through segmentation. Presented at GECCO '09: Proceedings of the 11th Genetic and Evolutionary Computation Conference

• 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