  As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces
promising performances in the field of anomaly detection. This paper presents
the application of the DCA to a standard data set, the KDD 99 data set. The
results of different implementation versions of the DXA, including the antigen
multiplier and moving time windows are reported. The real-valued Negative
Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision
tree algorithm are used, to conduct a baseline comparison. The results suggest
that the DCA is applicable to KDD 99 data set, and the antigen multiplier and
moving time windows have the same effect on the DCA for this particular data
set. The real-valued NSA with constant-sized detectors is not applicable to the
data set, and the C4.5 decision tree algorithm provides a benchmark of the
classification performance for this data set.
