Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher thanmany anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce false alarm rate. We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases.

中文翻译：

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基于常数检测器的改进的实值负选择算法用于异常检测

与传统的负选择算法相比，在整个状态空间中随机生成检测器，固定边界的负选择算法（FB-NSA）非随机地生成了一层紧密围绕自身空间的检测器。但是，FB-NSA的误报率高于许多异常检测方法。当正常数据接近状态空间的边界时，其检测率非常低。本文提出了一种改进的FB-NSA（IFB-NSA）来解决这些问题。IFB-NSA扩大了状态空间，并在适当的位置添加了辅助检测器以提高检测率，并使用可变大小的训练样本来降低误报率。我们目前在合成数据集和UCI Iris数据集上进行实验，以证明这种方法的有效性。