Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion

Abstract

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and LF-norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.