Migration Threshold Tuning In The Deterministic Dendritic Cell Algorithm

In this paper we explore the sensitivity of the migration threshold parameter in the Deterministic Dendritic Cell Algorithm (dDCA), one of the four main types of Artificial Immune System. This is with a view to the future construction of a DCA augmented with Deep Learning. Learning mechanisms are absent in the original DCA although tuneable parameters are identified which have the potential to be learned over time. Proposed in this paper is the necessary first step towards placing the dDCA within the context of Deep Learning by understanding the maximum migration threshold parameter. Tuning the maximum migration threshold determines the results of the signal processing within the algorithm, and here we explore a range of values. We use the previously explored Ping Scan Dataset to evaluate the influence of this key parameter. Results indicate a close relationship between the maximum migration threshold and the signal values of given datasets. We propose in future to ascertain an optimisation function which would learn the maximum migration threshold during run time. This work represents a necessary step towards producing a DCA which automatically interfaces with any given anomaly detection dataset.