Implicit learning in autoencoder novelty assessment

Abstract

When the situation arises that only "normal" behavior is known about a system, it is desirable to develop a system based solely on that behavior which enables the user to determine when that system behavior falls outside of that range of normality. A new method is proposed for detecting such novel behavior through the use of autoassociative neural network encoders, which can be shown to implicitly learn the nature of the underlying "normal" system behavior.