Structure regularized traffic monitoring model for traffic matrix estimation and anomaly detection

Abstract

We consider the network traffic monitoring issue, including traffic matrix estimation and anomaly detection problems. New model called structure regularized traffic monitoring (SRTM) is presented by incorporating the spatial and temporal properties of the traffic matrices, with the sparsity of the traffic volume anomalies. The proposed SRTM model adapts more naturally to the engineering nature of the real traffic data and imposes structural regularizations. This stems from the fact that the real traffic data is usually approximately instead of exactly low-rank and exhibits strong spatial and temporal dependencies. In addition, we take advantages of the sparsity of the traffic volume anomalies and impose sparse constraints on the variables to detect the traffic anomalies. Efficient algorithm that based on the proximal gradient and gradient descent methods are proposed to solve the SRTM optimization model. Experiments in synthetic networks are carried out to verify the feasibility and effectiveness of the proposed model.