Abstract:

In order to overcome the blindness of evaluation of the context in the classical Dendritic Cell Algorithm (DCA), how the weight matrix of DCA influences the detection results is analyzed, and two kinds of DCAs are proposed, which can adjust the false positives and false negatives. The first one is the improved voting DCA. The Tendency Factor (TF) is involved in the Dendritic Cell (DC) state transition to assess the context fairly, and through the fine adjustment of the TF the false positives and false negatives of the detection results are controlled. The other one is the scoring DCA. In DC state transition phase, the evaluation of the context is ignored, instead, the antigen is directly given a score, then according to the distribution of average scores of antigens the anomaly threshold value can be adjusted to control the false positives and false negatives. Experiments show that the two algorithms can both effectively realize controlling the results. Comparatively, the scoring DCA can realize more intuitive control.

Key words: dendritic cell algorithm;anomaly detection;tendency factor;artificial immune;data fusion