Developing a generic framework for anomaly detection

Highlights：

• We develop a generic linear one-class classifier fusion method for anomaly detection that can be effectively applied to different domains and applications.

• We propose a new score normalisation method to support multiple classifier fusion, even in the case of the training data having a heavy-tailed distribution.

• We show that the performance of anomaly detection methods is normalisation sensitive.

• We define a novel fitness function to measure the effectiveness of the fused anomaly detectors without the need for anomalous samples and propose a particle swarm optimisation method for its optimisation.

• We experimentally and statistically demonstrate that the proposed weighted averaging fusion achieves superior performance compared to the state-of-the-art methods.