An immune optimization based deterministic dendritic cell algorithm.

Anomaly detection is an important issue, which has been deeply studied in different research domains and application fields. The dendritic cell algorithm (DCA) is one of the most popular artificial immune system inspired approaches to handle anomaly detection problems. The performance of DCA depends significantly on the parameters used to compute the relationship between input instance and detectors. However, we find that while the DCA's performance is good in practical applications, it is difficult to analyze due to the empirical based parameters and lacks adaptability. This paper studies how to effectively learn appropriate parameters for deterministic DCA (dDCA) for anomaly detection tasks. In particular, we propose a novel immune optimization based dDCA (IO-dDCA) for anomaly detection. It consists of dDCA classification, T cell (TC) classification, gradient descent optimization and immune nonlinear dynamic optimization. First, the dDCA is regarded as a binary classifier, and the data instances which are labeled as normal will be classified by a T cell inspired classification method, so as to improve the classification performance of dDCA. Then, to improve dDCA's adaptability, gradient descent is adopted for dDCA parameters' optimization. Finally, the immune nonlinear model is introduced to adjust learning rate in gradient descent to find the optimal parameters. The theoretical and experimental performance analysis of IO-dDCA show effectiveness of the novel approach through simulations, and the experimental results show that the proposed IO-dDCA has good classification accuracy. [ABSTRACT FROM AUTHOR]