Towards a practical process model for anomaly detection systems.

Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machine learning need a supportive process model to guide the developers and stakeholders during the development process. One major problem type of machine learning is anomaly detection. Its goal is to identify anomalous data points (outlier) between the normal data instances. Anomaly detection has a wide scope of applications in industrial and scientific areas. Detecting intruders in computer networks, distinguishing between cancerous and healthy tissue in medical images, cleaning data from disturbing outliers for further evaluation and many more. The cross-industry standard process for data mining (CRISP-DM) has been developed to support developers with all kinds of data mining applications. It describes a generic model of six phases that covers the whole development cycle. The generality of the CRISP-DM model is as much a strength as it is a weakness, since the particularities of different problem types like anomaly detection can not be addressed without making the model overly complex. There is a need for a more practical, specialised process model for anomaly detection applications. We demonstrate this issue and outline an approach towards a practical process model tailored to the development of anomaly detection systems.