  The Dendritic Cell Algorithm is an immune-inspired algorithm orig- inally
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
anal- yse 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.
