Detecting Anomalies with Autoencoders on Data Streams

Lucas Cazzonelli, Cedric Kulbach

Abstract: Autoencoders have achieved impressive results in anomaly detection tasks by identifying anomalous data as instances that do not match their learned representation of normality. To this end, autoencoders are typically trained on large amounts of previously collected data before being deployed. However, in an online learning scenario, where a predictor has to operate on an evolving data stream and therefore continuously adapt to new instances, this approach is inadequate. Despite their success in offline anomaly detection, there has been little research leveraging autoencoders as anomaly detectors in such a setting. Therefore, in this work, we propose an approach for online anomaly detection with autoencoders and demonstrate its competitiveness against established online anomaly detection algorithms on multiple real-world datasets. We further address the issue of autoencoders gradually adapting to anomalies and thereby reducing their sensitivity to such data by introducing a simple modification to the models’ training approach. Our experimental results indicate that our solution achieves a larger gap between the losses on anomalous and normal instances than a conventional training procedure.