Multivariate Statistical Network Monitoring

Multivariate statistical network monitoring focuses on analyzing complex, high-dimensional data streams from interconnected systems to detect anomalies and understand system behavior. Current research emphasizes developing efficient algorithms, such as those based on collaborative machines and probabilistic PCA, to capture dependencies between variables and historical patterns for improved anomaly detection accuracy and speed. This field is crucial for various applications, including industrial process monitoring, network security, and scientific data analysis, enabling proactive fault detection and improved system reliability.