A scale-adaptive positive selection algorithm based on B-cell immune mechanisms for anomaly detection

Abstract

In current anomaly detection immune algorithms, the methods for setting the detection radius of detectors fail to take into account the concentration characteristic of self samples, which weaken their application effect. In response to this deficiency, we proposed a new type of detector named the scale-adaptive B-cells (SAB-cells) detector, and a novel algorithm named scale-adaptive positive selection algorithm (SA-PSA). This algorithm is mainly based on the B-cell immune mechanisms of clonal variation and network suppression. In SA-PSA, the detection radius of SAB-cells can be adaptively adjusted by clonal variation, and the number of redundant SAB-cells can be effectively compressed by fusion variation, so as to eventually obtain efficient detectors. Based on the Iris data set, firstly, we analyzed the effects of three main control parameters on SA-PSA; secondly, we compared SA-PSA with other mainstream anomaly detection immune algorithms by three performance indicators; thirdly, we performed the analysis of receiver operating characteristic (ROC) curve and verified the effectiveness of SA-PSA. At last, we also applied SA-PSA to bearing anomaly detection and further verified its effectiveness in more complicated engineering applications.