Rethinking Concepts of the Dendritic Cell Algorithm for Multiple Data Stream Analysis

This paper begins by stating that the underlying concepts of signals and antigen used by the Dendritic Cell Algorithm are too abstract and arbitrary to be of use in real world applications as they stand. To address this, these concepts are more explicitly defined within a specific application area, namely that of data stream analysis. These new definitions are based around the outputs of the Change Point Detecting Subspace Tracker (CD-ST), a recently developed algorithm for detecting key change points across multiple data streams. Preliminary results demonstrate the utility of this new definition for antigen. The paper concludes by laying the theoretical groundwork for a novel anomaly detection framework for use in data streaming applications. The underlying methodology is to perform anomaly detection via the detection and classification of key change points that occur across the multiple data streams monitored.