Dendritic Cell Algorithm for Anomaly Detection in Unordered Data Set

Abstract

The performance of the Dendritic Cell Algorithm (DCA) is promising in the ordered data set, however, with the context changing multiple times in quick succession there will be a sudden drop in the accuracy, and the rate of false positives and false negatives will increase significantly. A Multiplying and Merging Dendritic Cell Algorithm (MMDCA) is proposed in the light of the unordered data set in anomaly detection. Firstly the data set is multiplied n times, i.e., n instances are generated for each type of antigen, then each instance is assessed, and finally the n assessments of each type of antigen will be merged to get the final result. Experiments show that the algorithm presented has considerable detection accuracy and stable detection performance.