It is trained to recognize the normal detector behavior from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system.

In the project, we are addressing this task by leveraging several deep learning graph-based approaches, to detect anomalies within the Parallels Remote Application Server (RAS) user experience and ...

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years.