State of the Art Anomaly

Anomaly detection research focuses on identifying unusual patterns or events within datasets, aiming to improve system reliability and security across diverse applications. Current efforts concentrate on developing robust models, particularly deep learning architectures like autoencoders, variational autoencoders, and graph neural networks, that are resilient to data contamination and capable of handling high-dimensional, heterogeneous data, including time series and graph-structured data. These advancements are crucial for enhancing the accuracy and interpretability of anomaly detection in various fields, from industrial quality control and cybersecurity to traffic management and healthcare. Improved evaluation metrics that account for temporal dynamics are also a key area of focus.