Radial Basis Function Data Descriptor (RBFDD) Network: An Anomaly Detection Approach

In this paper, we propose a modification to the standard Ra-dial Basis Function (RBF) network that transforms it into a one-class classifier suitable for anomaly detection. We name this new approach the Radial Basis Function Data Descriptor (RBFDD) network. The RBFDD network is of interest as it has inherent adaptability in its architecture making it suitable for domains in which concept drift is a concern. Also, features learned by an RBFDD network (i.e., centers and spreads of Gaussian kernels and associated weights) provide us with a level of interpretability that has potential to be quite informative in terms of understanding the model learned and the reasoning behind flagging anomalies. In a set of evaluation experiments we compare the performance of the RBFDD network with some state of the art algorithms for anomaly detection over a collection of benchmark anomaly detection datasets. The results show that the RBFDD network is a promising approach and suggest potential for more investigations and promising directions for future work. We also investigate how RBFDD networks can be interpreted.