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

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Abstract

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.

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Acknowledgements

The authors wish to thank NSFC- http://www.nsfc.gov.cn/ for their support through Grant Number 61877045 and 62202147, Fundamental Research Project of Shenzhen Science and Technology Program for their support through Grant Number JCYJ20160428153956266, and Scientific Research Project of Hubei Provincial Department of Education for their support through Grant Number D20191406.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Zhe Ming & Yiwen Liang

  2. School of Computer Science, Hubei University of Technology, Wuhan, China

    Zhe Ming & Wen Zhou

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  1. Zhe Ming
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  2. Yiwen Liang
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  3. Wen Zhou
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Correspondence to Zhe Ming.

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Ming, Z., Liang, Y. & Zhou, W. NDAMM: a numerical differentiation-based artificial macrophage model for anomaly detection. Appl Intell 53, 16151–16169 (2023). https://doi.org/10.1007/s10489-022-04334-1

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