2020/04/20 by Ananda Streit, Gustavo H. A. Santos, Gustavo H. Santos +12
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #Tensor decomposition and applications #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.2004.09655
arxiv created 2020/04/20 · openalex publication_date 2020/04/20 · arxiv updated 2020/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of detecting anomalies in time series from network measurements has been widely studied and is a topic of fundamental importance. Many anomaly detection methods are based on packet inspection collected at the network core routers, with consequent disadvantages in terms of computational cost and privacy. We propose an alternative method in which packet header inspection is not needed. The method is based on the extraction of a normal subspace obtained by the tensor decomposition technique considering the correlation between different metrics. We propose a new approach for online tensor decomposition where changes in the normal subspace can be tracked efficiently. Another advantage of our proposal is the interpretability of the obtained models. The flexibility of the method is illustrated by applying it to two distinct examples, both using actual data collected on residential routers.