NDAMM: a numerical differentiation-based artificial macrophage model for anomaly detection

Anomaly detection is a significant issue that has attracted considerable research. The artificial immune system offers strong pattern recognition ability, adaptability and dynamic characteristics; therefore, it has been extensively used for anomaly detection. However, the boundary between normal and abnormal data patterns is difficult to define, which reduces the anomaly detection precisions of artificial immune approaches. Biological macrophages have a strong ability to identify various abnormalities, therefore, this study proposes a novel numerical differentiation-based artificial macrophage detection model (NDAMM) for anomaly detection. In particular, numerical differentiation is introduced in signal extraction, which can perceive signal changes more clearly and perform signal mapping. Furthermore, we design an artificial macrophage algorithm to determine weights based on input data and identify abnormalities using a signal fusion process. Finally, the proposed approach is implemented in anomaly detection. Through implementations on 20 anomaly detection datasets, the results of these experiments demonstrate that the NDAMM surpasses state-of-the-art anomaly detection methodologies. Ablation studies, parametric analysis, and statistical analysis are used to validate the effectiveness of our model.