The term \u201eanomaly detection\u201c is particularly important in the fields of Big Data and Smart Data, Artificial Intelligence, and Industry and Industry 4.0. It describes methods used to automatically detect deviations from normal patterns or behaviours in large datasets.<\/p>\n
Imagine thousands of machine hours being recorded daily in a modern factory. If a machine suddenly starts behaving differently \u2013 for example, due to unusually high temperatures or an unexpected stoppage \u2013 the system immediately recognises this deviation. In technical jargon, this deviation is called an \u201eanomaly\u201c. Anomaly detection helps to identify errors, fraud attempts or cyber-attacks at an early stage, thereby preventing damage or failures.<\/p>\n
At their core, programmes analyse collected data for anomalies that were not predictable. Anomaly detection also works outside of industry: for example, in online banking, when unusually large amounts are transferred and the system triggers an alarm.<\/p>\n
For companies, anomaly detection means increased security and rapid response capabilities in the event of problems. It is a modern tool for significantly better control of risks in digital processes.<\/p>","protected":false},"excerpt":{"rendered":"