A negative selection algorithm with hypercube interface detectors for anomaly detection.

Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters. • HI-detector breaks away from the hyperspherical detector used by traditional negative selection algorithms. • HI-detector consists of a cluster of multiple hypercube self-samples. • HI-detector completely eliminates duplicate coverage of holes and detectors. • HI-detector has only one parameter that needs to be set. [ABSTRACT FROM AUTHOR]