Detecting Anomaly

Anomaly detection aims to identify unusual patterns or data points deviating from established norms within various data types, including images, videos, time series, and network traffic. Current research emphasizes developing robust and efficient methods using diverse model architectures, such as graph neural networks, large language models, autoencoders, transformers, and recurrent neural networks, often tailored to specific data modalities and application contexts. This field is crucial for numerous applications, from enhancing cybersecurity and fraud detection to improving industrial process monitoring and medical diagnosis, with ongoing efforts focused on improving accuracy, efficiency, and explainability.