Anomalous Behavior

Anomalous behavior detection focuses on identifying deviations from expected patterns in diverse data streams, aiming to improve security, efficiency, and system understanding. Current research emphasizes the development of robust algorithms, including graph neural networks, autoencoders, and hybrid models combining machine learning with traditional statistical methods, applied to various data types such as time series, video, and network logs. This field is crucial for enhancing security in cloud services and smart homes, improving industrial process monitoring, and enabling more effective decision-making in areas like crowd management and healthcare.