用于异常检测的小参数集树突状细胞算法

树突状细胞算法（dendritic cell algorithm， DCA）是受自然免疫系统中树突状细胞的功能启发的免疫算法。当应用于实时异常检测时该算法具有优越的性能，但由于参数和随机元素相当多,算法难于分析。提出了一种用于异常检测的小参数集树突状细胞算法，在保证算法实现正确功能的前提下，减少了DCA中的参数，使算法参数数量得到了控制。此外，新算法还定义了更为简洁的信号处理过程以及对应的异常度量和异常阈值。最后，利用端口扫描数据集对算法进行了测试，实验结果表明，新算法是DCA的一种有效形式，新的异常度量更加敏感且它体现出的正确分类时间延长了30.3%~56.7%。

Small set of parameters dendritic cell algorithm for anomaly detection

The dendritic cell algorithm (DCA) is an immune-inspired algorithm based on the function of dendritic cells of the natural immune system. The algorithm performs very well when applied to real-time anomaly detection, but it is difficult to analyze the detected results because of a large number of parameters and stochastic elements. This paper presents a novel dendritic cell algorithm based on the small set of parameters for anomaly detection. It reduces the number of parameters of DCA on the premise of ensuring the correct function of the algorithm, so that the number of parameters is under control. Moreover, the new algorithm yet defines a more concise signal processing procedure, as well as the corresponding anomaly measure and an anomaly threshold. Finally, a port scan data set is applied to test the algorithm. Experimental results show that the new algorithm is an effective form of DCA, the new anomaly measurement is more sensitive and it extends the time of correct classification by 30.3%~56.7%.