  The Dendritic Cell Algorithm (DCA) as one of the emerging evolutionary
algorithms is based on the behavior of the specific immune agents; known as
Dendritic Cells (DCs). DCA has several potentially beneficial features for
binary classification problems. In this paper, we aim at providing a new
version of this immune-inspired mechanism acts as a semi-supervised classifier
which can be a defensive shield in network intrusion detection problem. Till
now, no strategy or idea has already been adopted on the GetAntigen() function
on detection phase, but randomly sampling entails the DCA to provide
undesirable results in several cycles in each time. This leads to uncertainty.
Whereas it must be accomplished by biological behaviors of DCs in tissues, we
have proposed a novel strategy which exactly acts based on its immunological
functionalities of dendritic cells. The proposed mechanism focuses on two
items: First, to obviate the challenge of needing to have a preordered antigen
set for computing danger signal, and the second, to provide a novel
immune-inspired idea in order to non-random data sampling. A variable
functional migration threshold is also computed cycle by cycle that shows
necessity of the Migration threshold (MT) flexibility. A significant criterion
so called capability of intrusion detection (CID) used for tests. All of the
tests have been performed in a new benchmark dataset named UNSW-NB15.
Experimental consequences demonstrate that the present schema dominates the
standard DCA and has higher CID in comparison with other approaches found in
literature.
