Based Anomaly Detection

Based anomaly detection focuses on identifying unusual patterns in data by learning the characteristics of "normal" data and flagging deviations from this learned norm. Current research emphasizes unsupervised methods, frequently employing autoencoders, support vector machines, and various tree-based algorithms, often enhanced by techniques like transfer learning, incremental learning, and hybrid approaches combining deep learning with classical methods. This field is crucial for diverse applications, ranging from cybersecurity and industrial process monitoring to medical diagnostics and infrastructure maintenance, enabling early detection of faults, attacks, or unusual events that might otherwise go unnoticed.