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The Deterministic Dendritic Cell Algorithm

  • Julie Greensmith
  • Uwe Aickelin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)

Abstract

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

Keywords

Danger Signal Anomaly Detection Safe Signal Antigen Type Deterministic Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Julie Greensmith
    • 1
  • Uwe Aickelin
    • 1
  1. 1.Intelligent Modeling and Analysis, School of Computer ScienceUniversity of NottinghamUK

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