Anomalous Event

Anomalous event detection focuses on identifying unusual patterns or outliers in diverse data streams, aiming to improve system reliability, security, and decision-making. Current research emphasizes unsupervised and semi-supervised machine learning approaches, leveraging techniques like autoencoders, graph neural networks, and clustering algorithms, often enhanced by explainability methods to improve human understanding of detected anomalies. This field is crucial for various applications, from cybersecurity and healthcare diagnostics to industrial process monitoring and autonomous systems, enabling proactive responses to unexpected events and improving overall system performance.