Detecting Anomalies in Information Assets with Graph Signal Processing

The significant business requirement for organizations utilizing technology-based services is the production of appropriate services and applications according to business needs, and the sustainable operation of the infrastructures providing these services. Even as organizations increasingly procure services externally due to new technological advancements, there remains a need to monitor Information Technology structures, configuration, and version requirements. The Information Technology Infrastructure Library (ITIL) and its subset, the Configuration Management Database (CMDB), provide a roadmap for these approaches. However, as technologies advance, this management need can become even more complex. Recently, with the increasing use of graph-based perspectives, both in artificial intelligence studies and in relational complex systems such as social networks and network structures, managing definitions in multidimensional space systems, such as objects in knowledge management systems using graph algorithms, has become easier. Within these studies, well-known artificial intelligence tasks such as prediction, classification, or anomaly detection can be more controllable.