Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

In this paper we present a Dendritic Cell Algorithm (DCA) approach to information fusion, combining key elements of immunological theory with the engineering principles of data fusion. In the human immune system, DCs have the power to suppress or activate the immune system by correlation of signals representing their environment, combined with locality markers in the form of antigens. Antigens are proteins in structure and are any protein to which the immune system can potentially respond. These cells are responsible for the detection of pathogens in the human body through the correlation of information (in the form of molecular signals) within the environment. By using an abstraction of DC behaviour, similar detection properties are shown, resulting in an algorithm capable of performing anomaly detection.