Extracting Baseline Patterns in Internet Traffic Using Robust Principal Components
Source: Colorado State University
Robust Base-Line (RBL) is a formal technique for extracting the baseline of network traffic to capture the underlying traffic trend. A range of applications such as anomaly detection and load balancing rely on baseline estimation. Once the fundamental period of the pattern for analysis is recognized, e.g., based on user interest or a period detector such as Autocorrelation Function (ACF), the basic extraction is carried out in two steps. First, the common component across the dataset is separated using Robust Principal Component Analysis (RPCA). The fundamental pattern in the common component is extracted using Principal Component Analysis (PCA) in the second step. Scaling factors required to fit the base-pattern back into the data are returned automatically by PCA.