Unsupervised Anomaly Detection With Specified 6

Unsupervised anomaly detection is a technique for identifying abnormal patterns in unlabelled data, where the anomaly ratio is set to 0.1%. This method aims to automatically detect data points that deviate from the normal range by learning the distribution characteristics of normal data, thereby achieving efficient recognition of rare anomalous events. Its application value lies in its ability to be widely used in industrial monitoring, cybersecurity, medical diagnosis, and other fields, enhancing the robustness and security of systems.