Abstract
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 than many 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 the 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.
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