Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
Tomasz Andrysiak, Łukasz Saganowski · Digital Repository of UTP in Bydgoszcz (Uniwersytet Technologiczno-Przyrodniczy) · 2016
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.